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by Ian Goodfellow, Yoshua Bengio and Aaron Courville 
        
            
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                        Hardcover                        ISBN: 9780262035613                        Pub date: November 18, 2016                        Publisher: The MIT Press                         800 pp., 7 x 9 in, 66 color illus., 100 b&amp;w illus.                                            
                    
                
            
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Deep Learning
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https://www.amazon.com/Mathematics-Machine-Learning-Peter-Deisenroth/dp/110845514X
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https://www.amazon.com/Machine-Learning-Design-Patterns-Preparation/dp/1098115783/
Amazon.com: Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps: 9781098115784: Lakshmanan, Valliappa, Robinson, Sara, Munn, Michael: Books
2020-11-24
Michael Munn Michael Munn
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<div><div><div><p><h3>Reviews with images</h3></p></div><div><ol><li><div><p></p></div></li><li><div><p></p></div></li></ol></div></div><div><div><div><div><p></p></div><div><p>Recommended for MLE and MLOps</p><div><p>Very useful book for anyone looking for best practices, especially when it comes to anyone moving from data science to machine learning engineering job or MLOps. Don’t expect explanations for algorithms here, but mainly how to deploy your model, monitor them, plan their training, etc.For me, lots of the information are not new to me, guess this will be the case for anyone who has been in the same field for a while already, however I find new stuff to learn every chapter still.I have seen criticism for the book for being too Google-centric. I can see that, with all the examples based on Big Query, TensorFlow, etc. But that did not bother me at all despite the fact that I haven’t used Google Cloud technology before. For me it was easy to generalise and see the concepts and ignore the specifics of Google’s products and their implementation.</p></div></div></div><p></p></div><div><p>Sorry we couldn't load the review</p></div></div></div>
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 Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps 1st Edition

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 * Book Description
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 The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.

 In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.

 You'll learn how to:

 * Identify and mitigate common challenges when training, evaluating, and deploying ML models
 * Represent data for different ML model types, including embeddings, feature crosses, and more
 * Choose the right model type for specific problems
 * Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning
 * Deploy scalable ML systems that you can retrain and update to reflect new data
 * Interpret model predictions for stakeholders and ensure models are treating users fairly

 Read more
 Continue reading Read less

 About the Author

 Valliappa (Lak) Lakshmanan is Global Head for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud's data analytics and machine learning products. He founded Google's Advanced Solutions Lab ML Immersion program. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA.

 Sara Robinson is a Developer Advocate on Google's Cloud Platform team, focusing on machine learning. She inspires developers and data scientists to integrate ML into their applications through demos, online content, and events. Sara has a bachelor’s degree from Brandeis University. Before Google, she was a Developer Advocate on the Firebase team.

 Michael Munn is an ML Solutions Engineer at Google where he works with customers of Google Cloud on helping them design, implement, and deploy machine learning models. He also teaches an ML Immersion Program at the Advanced Solutions Lab. Michael has a PhD in mathematics from the City University of New York. Before joining Google, he worked as a research professor.
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 1. ISBN-10
 1098115783
 2. ISBN-13
 978-1098115784
 3. Edition
 1st
 4. Publisher
 O'Reilly Media
 5. Publication date
 November 24, 2020
 6. Language
 English
 7. Dimensions
 9.06 x 0.94 x 6.85 inches
 8. Print length
 405 pages
 9. See all details
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 About This Book

 In engineering disciplines, design patterns capture best practices and solutions to commonly occurring problems. They codify the knowledge and experience of experts into advice that all practitioners can follow. This book is a catalog of machine learning design patterns that we have observed in the course of working with hundreds of machine learning teams.

 Who Is This Book For?

 Introductory machine learning books usually focus on the what and how of machine learning (ML). They then explain the mathematical aspects of new methods from AI research labs and teach how to use AI frameworks to implement these methods.

 This book, on the other hand, brings together hard-earned experience around the “why” that underlies the tips and tricks that experienced ML practitioners employ when applying machine learning to real-world problems.

 We assume that you have prior knowledge of machine learning and data processing. This is not a fundamental textbook on machine learning. Instead, this book is for you if you are a data scientist, data engineer, or ML engineer who is looking for a second book on practical machine learning.

 If you already know the basics, this book will introduce you to a catalog of ideas, some of which you may recognize, and give those ideas a name so that you can confidently reach for them. If you're a computer science student headed for a job in industry, this book will round out your knowledge and prepare you for the professional world. It will help you learn how to build high-quality ML systems.

 What’s Not in the Book

 This is a book that is primarily for ML engineers in the enterprise, not ML scientists in academia or industry research labs.

 We purposefully don't discuss areas of active research—you will find very little here, for example, on machine learning model architecture (bidirectional encoders, or the attention mechanism, or short-circuit layers, for example) because we assume that you will be using a pre-built model architecture (Ex: ResNet-50 or GRUCell), not writing your own image classification or recurrent neural network.

 Here are some concrete examples of areas that we intentionally stay away from because we believe that these topics are more appropriate for college courses and ML researchers:

 ML algorithms -- We do not cover the differences between random forests and neural networks, for example. This is covered in introductory machine learning textbooks.

 Building blocks -- We do not cover different types of gradient descent optimizers or activation functions. We recommend using Adam and ReLU—in our experience, the potential for improvements in performance by making different choices in these sorts of things tends to be minor.

 ML model architectures -- If you are doing image classification, we recommend that you use an off-the-shelf model like ResNet or whatever the latest hotness is at the time you are reading this. Leave the design of new image classification or text classification models to researchers who specialize in this problem.

 Model layers -- You won’t find convolutional neural networks or recurrent neural networks in this book. They are doubly disqualified—first, for being a building block and second, for being something you can use off-the-shelf.

 Custom training loops -- Just calling model.fit() in Keras will fit the needs of practitioners.

 In this book, we have tried to include only common patterns of the kind that machine learning engineers in enterprises will employ in their day-to-day work.

 As an analogy, consider data structures. While a college course on data structures will delve into the implementations of different data structures, and a researcher on data structures will have to learn how to formally represent their mathematical properties, the practitioner can be more pragmatic. An enterprise software developer simply needs to know how to work effectively with arrays, linked lists, maps, sets, and trees. It is for a pragmatic practitioner in machine learning that this book is written.

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 * Publisher ‏ : ‎ O'Reilly Media; 1st edition (November 24, 2020)
 * Language ‏ : ‎ English
 * Paperback ‏ : ‎ 405 pages
 * ISBN-10 ‏ : ‎ 1098115783
 * ISBN-13 ‏ : ‎ 978-1098115784
 * Item Weight ‏ : ‎ 1.45 pounds
 * Dimensions ‏ : ‎ 9.06 x 0.94 x 6.85 inches
 * Best Sellers Rank: #53,247 in Books ( See Top 100 in Books )
 + #9 in Machine Theory (Books)
 + #10 in Business Intelligence Tools
 + #113 in Artificial Intelligence & Semantics
 * Customer Reviews:
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 Lak is Head for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud's data analytics and machine learning products. He is the author of Machine Learning Design Patterns, Data Science on GCP (O'Reilly), BigQuery the Definitive Guide (O'Reilly). He founded Google's Advanced Solutions Lab ML Immersion program. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA. He's the original author of several Coursera specializations including Machine Learning on GCP, Advanced Machine Learning on GCP, and Data Engineering.

 Follow him on Twitter at @lak_luster.

 http://www.vlakshman.com/

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  Customer reviews

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  5 Stars
  Recommended for MLE and MLOps
  Very useful book for anyone looking for best practices, especially when it comes to anyone moving from data science to machine learning engineering job or MLOps. Don’t expect explanations for algorithms here, but mainly how to deploy your model, monitor them, plan their training, etc.For me, lots of the information are not new to me, guess this will be the case for anyone who has been in the same field for a while already, however I find new stuff to learn every chapter still.I have seen criticism for the book for being too Google-centric. I can see that, with all the examples based on Big Query, TensorFlow, etc. But that did not bother me at all despite the fact that I haven’t used Google Cloud technology before. For me it was easy to generalise and see the concepts and ignore the specifics of Google’s products and their implementation.
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  Jason in Chicago
  5.0 out of 5 stars A useful book for a n00b like me with a background in programming
  Reviewed in the United States on December 17, 2020
  Verified Purchase
  My background: I'm an expert software engineer (C++, Java, etc) and proud n00b at machine learning. I've read the O'Reilly "AI and Machine Learning for Coders" book and many online articles. I have a background in trading/financial software, which exposed me to many statistical terms in this book. In the past, PhD level physics/math quants would typically handle those topics and this book has helped me realize some gaps in my knowledge and fill them (sometimes via online search). I can now at least reason about those concepts better even if I don't yet understand the details.

  I'm 1/3 into the book (so maybe premature for 5 stars) and it's been a dense but interesting read so far. There have been times where I have to lookup terms but the material has still been approachable. The language in the first couple chapters could probably be simplified some but it was sufficient for me with a lot of coffee. I expect to still have very incomplete knowledge after finishing this book due to lack of practical experience. However, my goal is to build a large scaffolding of knowledge/concepts on ML that I can use as a foundation for future learning and broaden my toolbox before I start hacking code. When I was learning C++, I found the Gang of Four book "Design Patterns" accomplished a similar goal to help bridge the gap between academic knowledge and practical software engineering. Much like with the GoF book I suspect I may be re-reading parts of this book in the future when my knowledge has matured. Some may prefer doing a lot of ML coding before reading this book, but I like to have a lot of background knowledge/tools before tackling code - personal preference I guess.

  I seem to have discovered an error/typo regarding "precision" vs "recall" in chapter 3:
  Page 135 paragraph 2: "If we care more that our model is correct whenever it makes a positive class prediction we'd optimize our prediction threshold for recall".
  I think the last word in that sentence should be "precision". The terms are defined on page 124 paragraph 2.
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  26 people found this helpful
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  R. B.
  5.0 out of 5 stars Excellent
  Reviewed in the United States on May 28, 2023
  Verified Purchase
  This is well written with fabulous examples throughout. It was reassuring to me to see patterns that I use in practice are documented here and there were plenty of inspirational ideas, too.
  Helpful
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  Stephen Zhou
  4.0 out of 5 stars Has some helpful uses
  Reviewed in the United States on August 6, 2023
  Verified Purchase
  The books is mostly from a computer science prospective. I am from an engineering background so my review may be biased. It covers design patters for data treatment, model design to MLOPs. I like the first two sections and my review is based on them. It provides alternative design patterns that I did not know before and they are purely practical. No theories involved
  One person found this helpful
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  Erik Meade
  5.0 out of 5 stars Good to fill gaps in knowledge and become aware of many things that are done subconsciously
  Reviewed in the United States on July 17, 2021
  Verified Purchase
  I thought this was a great book for providing people with an understanding of the toolkit that ML engineers need to know when making Machine Learning models.

  As a side note, I bought this to be better prepared for ML architecture and design interviews.

  If you are in a hurry, I think the content in Chapters 2, 3, and 4 are great. 5 was somewhat relevant for me and Chapters 6, and 7 are not really relevant until you are actually neck-deep in the models, so they did not really apply to me.

  Chapter 8 was fantastic since it had a Common Patterns by Use Case and Data Type section, and enumerated many different types of problems and the tools that one might use to tackle them.

  I am satisfied with what I got from this book.
  7 people found this helpful
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  Ernesto Mancebo
  5.0 out of 5 stars Must have as Data Scientist
  Reviewed in the United States on June 13, 2021
  Verified Purchase
  This book contains a lot of good practices in a easy to read way, so you don't have to digest all the white papers online. I'd love to have the e-book version so I could read some hints while I run the Jupyter Notebooks, but seems that the publisher doesn't allows you to get the e-book with the book so you must buy both.
  3 people found this helpful
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  javierokc
  5.0 out of 5 stars Must read for serious machine learning developers
  Reviewed in the United States on January 12, 2021
  Verified Purchase
  This is a must-read for scientists and practitioners looking to apply machine learning theory to real life problems. I foresee this book becoming a classical of the discipline’s literature. Very well written and comprehensive description of concepts and applications of design patterns.
  3 people found this helpful
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  Xiaonan Lu
  5.0 out of 5 stars The Long-awaited ML design patterns book
  Reviewed in the United States on January 13, 2021
  Verified Purchase
  Got to know this book from a LinkedIn book review and thought it would fill in the gap of practical ML design patterns.
  Finished 4/5 of the book and expect to keep it close to my desk. Highly recommended.
  3 people found this helpful
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  Cole Holcomb
  4.0 out of 5 stars A solid resource for ML practitioners of all skill levels
  Reviewed in the United States on May 10, 2021
  Verified Purchase
  I read the book cover-to-cover and found it very useful in filling some of my knowledge gaps (e.g., some feature engineering techniques for categorical inputs) as well as refreshing some topics I'm already familiar with. I would probably rate this at 5 stars, except that roughly half of the figures/images are difficult to read based on the print quality.
  4 people found this helpful
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  Denisse Benitez
  5.0 out of 5 stars Llego en muy buenas condiciones
  Reviewed in Mexico on January 6, 2023
  Verified Purchase
  Era un regalo para mi novio, lo tuve que devolver porque se lo regalaron repetido!
  Aun asi, llego en muy buenas condiciones y buen tiempo de entrega, gracias!
  Report
  Translate review to English
  theFatih
  3.0 out of 5 stars ML Design Patterns with Google Cloud AI
  Reviewed in Canada on February 15, 2021
  Verified Purchase
  It is a good book if you are using Google Cloud AI.
  It covers many important design patterns for MLE and MLOps that you cannot find in other books.
  This would be a great book if you are planning to use Google products.
  Cover should have specified that it is written for Google Cloud AI.
  I returned it as I prefer to use open-source for this ever-changing field.
  4 people found this helpful
  Report
  yabo
  5.0 out of 5 stars Great book! Really interesting to see how the industry develops ML best practices
  Reviewed in Germany on January 4, 2022
  Verified Purchase
  This book was inspirational. It is very well structured and provides clear explanation on when a pattern is useful and the alternative you have as an ML practitioner. The book is biased more towards Google Cloud offering and Tenserflow. They sometimes offer alternatives on AWS/Azure and PyTorch -- but not very often.
  Report
  Tarek A
  4.0 out of 5 stars Good tips in here
  Reviewed in the Netherlands on January 1, 2022
  Verified Purchase
  I am still halfway through the book, but I already find it useful. Many things are usually common knowledge to anyone who has been in the field for a while, but still every section I encounter a good tip. Some reviews say it’s Google/TensorFlow focused, but for me despite not using GCP, this wasn’t a problem still. All tips here can be generalised to other frameworks easily
  Report
  soma sekhar dhavala
  3.0 out of 5 stars It should be read, instead as, ML Best Practices Cookbook
  Reviewed in India on November 22, 2021
  Verified Purchase
  "ML Design Patterns" is a misleading title.

  I was very excited to read it cover-to-cover after checking the title, and that the authors drew parallels to Design Patterns in Software Engineering.

  Their patterns looked more like hacks/tricks than Design Patterns. I still am not sure what/how exactly Design Pattern should be -- but certainly they should go beyond proving tips and tricks.

  For example, I was more interested in "problem abstractions" and then provide a map to a "solution template". Lot of techniques that exist in the wild today can be mapped to simple problem types, and as a results, same technique can be applied, and avoid reinventing the wheel -- think of "canonical" forms in the optimisation literature. Similar things exists in Statistics literature also. From models persepctive, the likes of Linear Models, Generalized Linear Models, Structural Equation Models, Seemingly Unrelated Regressions, Measurement-Error-in-Predcictors etc. There are quite a many. While it is impossible to create a taxonomy out of it, at least, I hoped some exercise in that direction would have taken place.

  What I was looking for in Design Patterns is:

  <IF> your response is binary (0/1), both features are categorical, objective is to predict responses at the unobserved combinations.
  <Then>
  Solution template

  The above problem is a "pattern" -- Collaborative Filtering, Netflix movie type, Item Response Theory, Logistic Regression -- all are different names they go with, depending on the reader's familiarity/ domain knowledge.

  The above is simply the "model" dimension. They are are other dimensions concerned with data, pre-processing, evaluation etc.

  In summary, I think that Design Patterns is a too strong word they used and probably they have not done justice to it. Instead, someone should read it as a cookbook of ML Best Practices, and things to watch out for, while implementing (not so much of a design). From that perspective, this book does justice.
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Reviews with images                            Recommended for MLE and MLOps  Very useful book for anyone looking for best practices, especially when it comes to anyone moving from data science to machine learning engineering job or MLOps. Don’t expect explanations for algorithms here, but mainly how to deploy your model, monitor them, plan their training, etc.For me, lots of the information are not new to me, guess this will be the case for anyone who has been in the same field for a while already, however I find new stuff to learn every chapter still.I have seen criticism for the book for being too Google-centric. I can see that, with all the examples based on Big Query, TensorFlow, etc. But that did not bother me at all despite the fact that I haven’t used Google Cloud technology before. For me it was easy to generalise and see the concepts and ignore the specifics of Google’s products and their implementation.        Sorry we couldn't load the review
Amazon.com: Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps: 9781098115784: Lakshmanan, Valliappa, Robinson, Sara, Munn, Michael: Books
https://www.amazon.com/Machine-Learning-Design-Patterns-Preparation/dp/1098115783/
https://www.amazon.com/Deep-Learning-Approach-Andrew-Glassner/dp/1718500726
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https://www.sas.com/en_us/insights/analytics/machine-learning.html
Machine Learning: What it is and why it matters
2022-06-29
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<div><div>
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<div><div><ol>
	<li>
	<a href="https://www.sas.com/en_us/insights.html">
	 SAS Insights
	</a>
	 
	</li>
	<li>
	<a href="https://www.sas.com/en_us/insights/analytics.html">
	 Analytics Insights 
	</a>
	 
	</li>
	</ol></div>
<div><p>
</p><h2>What it is and why it matters</h2>
<p></p></div>
<div><p>Machine learning is a method of data analysis that automates analytical model building. It is a branch of <a href="https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html">artificial intelligence</a> based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.</p></div>
<div><div>
<h2>Evolution of machine learning</h2>
<p>Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.<br />
</p>
<p>While many machine learning <a href="https://www.sas.com/en_us/insights/articles/analytics/machine-learning-algorithms-guide.html">algorithms have been around for a long time</a>, the ability to automatically apply complex mathematical calculations to <a href="https://www.sas.com/en_us/insights/big-data/what-is-big-data.html">big data</a> – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:</p>
<ul><li>The heavily hyped, self-driving Google car? The essence of machine learning.</li><li>Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.</li><li>Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.</li><li>Fraud detection? One of the more obvious, important uses in our world today.<br />
</li></ul>
</div>
<div>
<h3>Machine Learning and Artificial Intelligence<br />
</h3>
<p>While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Watch this video to better understand the relationship between AI and machine learning. You'll see how these two technologies work, with useful examples and a few funny asides.<br />
</p>
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</p><h3>Read more about this topic<br />
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Analytics tackles the scourge of human traffickingVictims of human trafficking are all around us. From forced labor to sex work, modern-day slavery thrives in the shadows. Learn why organizations are turning to AI and big data analytics to unveil these crimes and change future trajectories.
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Viking transforms its analytics strategy using SAS® Viya® on AzureViking is going all-in on cloud-based analytics to stay competitive and meet customer needs. The retailer's digital transformation are designed to optimize processes and boost customer loyalty and revenue across channels.
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Public health infrastructure desperately needs modernizationPublic health agencies must flex to longitudinal health crises and acute emergencies – from natural disasters like hurricanes to events like a pandemic. To be prepared, public health infrastructure must be modernized to support connectivity, real-time data exchanges, analytics and visualization.
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SAS CIO: Why leaders must cultivate curiosity in 2021With the change we’re all facing this year, CIOs should be counting on curiosity to play a crucial role in how we’re going to meet the challenges that lie ahead. From the moment COVID-19 hit, our IT organization has relied on curiosity – that strong desire to explore, learn, know - to fuel the urgent changes required. And it’s curiosity that will enable us to meet the needs of the future of work post-pandemic.
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<div><div>
<h2>What are some popular machine learning methods?</h2>
<p>Two of the most widely adopted machine learning methods are <b>supervised learning</b> and <b>unsupervised learning</b> – but there are also other methods of machine learning. Here's an overview of the most popular types.<br />
</p>
</div>
<div><div>
<p><b>Supervised learning </b>algorithms are trained using labeled examples, such as an input where the desired output is known. For example, a piece of equipment could have data points labeled either “F” (failed) or “R” (runs). The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. It then modifies the model accordingly. Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data. Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.</p>
<p><b>Unsupervised learning </b>is used against data that has no historical labels. The system is not told the "right answer." The algorithm must figure out what is being shown. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns. Or it can find the main attributes that separate customer segments from each other. Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition. These algorithms are also used to segment text topics, recommend items and identify data outliers.</p>
</div><div>
<p><b>Semisupervised learning </b>is used for the same applications as supervised learning. But it uses both labeled and unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data (because unlabeled data is less expensive and takes less effort to acquire). This type of learning can be used with methods such as classification, regression and prediction. Semisupervised learning is useful when the cost associated with labeling is too high to allow for a fully labeled training process. Early examples of this include identifying a person's face on a web cam.<br />
</p>
<p><b>Reinforcement learning</b> is often used for robotics, gaming and navigation. With reinforcement learning, the algorithm discovers through trial and error which actions yield the greatest rewards. This type of learning has three primary components: the agent (the learner or decision maker), the environment (everything the agent interacts with) and actions (what the agent can do). The objective is for the agent to choose actions that maximize the expected reward over a given amount of time. The agent will reach the goal much faster by following a good policy. So the goal in reinforcement learning is to learn the best policy.</p>
</div></div><div>
<blockquote>Humans can typically create one or two good models a week; machine learning can create thousands of models a week.</blockquote>
</div>
<div><p><strong>Thomas H. Davenport</strong>, Analytics thought leader<br />
<em>excerpt from The Wall Street Journal</em></p></div>
</div>
</div>
</main>
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 1. SAS Insights
 2. Analytics Insights
 * Importance
 * Today's World
 * Who Uses It
 * How It Works
 Best Practices

 Machine Learning

 What it is and why it matters

 Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

 * Importance
 * 
 * Today's World
 * 
 * Who Uses It
 * 
 * How It Works

 Evolution of machine learning

 Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.

 While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:

 * The heavily hyped, self-driving Google car? The essence of machine learning.
 * Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.
 * Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
 * Fraud detection? One of the more obvious, important uses in our world today.


 Machine Learning and Artificial Intelligence

 While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Watch this video to better understand the relationship between AI and machine learning. You'll see how these two technologies work, with useful examples and a few funny asides.

 Why is machine learning important?

 Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.

 All of these things mean it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.


 What's required to create good machine learning systems?

 * Data preparation capabilities.
 * Algorithms – basic and advanced.
 * Automation and iterative processes.
 * Scalability.
 * Ensemble modeling.

 Did you know?

 * In machine learning, a target is called a label.
 * In statistics, a target is called a dependent variable.
 * A variable in statistics is called a feature in machine learning.
 * A transformation in statistics is called feature creation in machine learning.

 Machine learning in today's world

 By using algorithms to build models that uncover connections, organizations can make better decisions without human intervention. Learn more about the technologies that are shaping the world we live in.

 Opportunities and challenges for machine learning in business

 This O'Reilly white paper provides a practical guide to implementing machine-learning applications in your organization.

 Read white paper

 Expand your skill set

 Get in-depth instruction and free access to SAS Software to build your machine learning skills. Courses include: 14 hours of course time, 90 days free software access in the cloud, a flexible e-learning format, with no programming skills required.

 Machine learning courses

 Will machine learning change your organization?

 This Harvard Business Review Insight Center report looks at how machine learning will change companies and the way we manage them.

 Download report

 Applying machine learning to IoT

 Machine learning can be used to achieve higher levels of efficiency, particularly when applied to the Internet of Things. This article explores the topic.


 Read the IoT article

 Who's using it?

 Most industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over competitors.

 Financial services

 Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cybersurveillance to pinpoint warning signs of fraud.

 Government

 Government agencies such as public safety and utilities have a particular need for machine learning since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine learning can also help detect fraud and minimize identity theft.

 Health care

 Machine learning is a fast-growing trend in the health care industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient's health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment.

 Retail

 Websites recommending items you might like based on previous purchases are using machine learning to analyze your buying history. Retailers rely on machine learning to capture data, analyze it and use it to personalize a shopping experience, implement a marketing campaign, price optimization, merchandise planning, and for customer insights.

 Oil and gas

 Finding new energy sources. Analyzing minerals in the ground. Predicting refinery sensor failure. Streamlining oil distribution to make it more efficient and cost-effective. The number of machine learning use cases for this industry is vast – and still expanding.

 Transportation

 Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations.

 Learn More About Industries Using This Technology

 Select industry
 * Agriculture
 * Banking
 * Capital Markets
 * Education
 * Health Care
 * Hotels
 * Insurance
 * Life Sciences
 * Manufacturing
 * Public Sector
 * Retail & Consumer Goods
 * Small & Midsize Business
 * Sports Analytics
 * Travel & Transportation
 * Telecom, Media & Technology
 * Utilities

 How it works

 To get the most value from machine learning, you have to know how to pair the best algorithms with the right tools and processes. SAS combines rich, sophisticated heritage in statistics and data mining with new architectural advances to ensure your models run as fast as possible – even in huge enterprise environments.

 Algorithms: SAS graphical user interfaces help you build machine learning models and implement an iterative machine learning process. You don't have to be an advanced statistician. Our comprehensive selection of machine learning algorithms can help you quickly get value from your big data and are included in many SAS products. SAS machine learning algorithms include:

 
 
 Neural networks 
 
 Decision trees 
 
 Random forests 
 
 Associations and sequence discovery 
 
 Gradient boosting and bagging 
 
 Support vector machines 
 
 Nearest-neighbor mapping 
 
 k-means clustering 
 
 Self-organizing maps 
 
 
 
 
 Local search optimization techniques (e.g., genetic algorithms) 
  
 Expectation maximization 
  
 Multivariate adaptive regression splines 
  
 Bayesian networks 
  
 Kernel density estimation 
  
 Principal component analysis 
  
 Singular value decomposition 
  
 Gaussian mixture models 
  
 Sequential covering rule building 
  
 

 Tools and Processes: As we know by now, it’s not just the algorithms. Ultimately, the secret to getting the most value from your big data lies in pairing the best algorithms for the task at hand with:

 
 
 Comprehensive data quality and management 
  
 GUIs for building models and process flows 
  
 Interactive data exploration and visualization of model results 
  
 Comparisons of different machine learning models to quickly identify the best one 
  
 
 
 
 Automated ensemble model evaluation to identify the best performers 
  
 Easy model deployment so you can get repeatable, reliable results quickly 
  
 An integrated, end-to-end platform for the automation of the data-to-decision process 
  
 

 Do you need some basic guidance on which machine learning algorithm to use for what? This blog by Hui Li, a data scientist at SAS, provides a handy cheat sheet.

 Read more about this topic

 * Analytics tackles the scourge of human trafficking Victims of human trafficking are all around us. From forced labor to sex work, modern-day slavery thrives in the shadows. Learn why organizations are turning to AI and big data analytics to unveil these crimes and change future trajectories.
 * Viking transforms its analytics strategy using SAS® Viya® on Azure Viking is going all-in on cloud-based analytics to stay competitive and meet customer needs. The retailer's digital transformation are designed to optimize processes and boost customer loyalty and revenue across channels.
 * Public health infrastructure desperately needs modernization Public health agencies must flex to longitudinal health crises and acute emergencies – from natural disasters like hurricanes to events like a pandemic. To be prepared, public health infrastructure must be modernized to support connectivity, real-time data exchanges, analytics and visualization.
 * SAS CIO: Why leaders must cultivate curiosity in 2021 With the change we’re all facing this year, CIOs should be counting on curiosity to play a crucial role in how we’re going to meet the challenges that lie ahead. From the moment COVID-19 hit, our IT organization has relied on curiosity – that strong desire to explore, learn, know - to fuel the urgent changes required. And it’s curiosity that will enable us to meet the needs of the future of work post-pandemic.

 What are some popular machine learning methods?

 Two of the most widely adopted machine learning methods are supervised learning and unsupervised learning – but there are also other methods of machine learning. Here's an overview of the most popular types.

 Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known. For example, a piece of equipment could have data points labeled either “F” (failed) or “R” (runs). The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. It then modifies the model accordingly. Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data. Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.

 Unsupervised learning is used against data that has no historical labels. The system is not told the "right answer." The algorithm must figure out what is being shown. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns. Or it can find the main attributes that separate customer segments from each other. Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition. These algorithms are also used to segment text topics, recommend items and identify data outliers.

 Semisupervised learning is used for the same applications as supervised learning. But it uses both labeled and unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data (because unlabeled data is less expensive and takes less effort to acquire). This type of learning can be used with methods such as classification, regression and prediction. Semisupervised learning is useful when the cost associated with labeling is too high to allow for a fully labeled training process. Early examples of this include identifying a person's face on a web cam.

 Reinforcement learning is often used for robotics, gaming and navigation. With reinforcement learning, the algorithm discovers through trial and error which actions yield the greatest rewards. This type of learning has three primary components: the agent (the learner or decision maker), the environment (everything the agent interacts with) and actions (what the agent can do). The objective is for the agent to choose actions that maximize the expected reward over a given amount of time. The agent will reach the goal much faster by following a good policy. So the goal in reinforcement learning is to learn the best policy.

 Humans can typically create one or two good models a week; machine learning can create thousands of models a week.

 Thomas H. Davenport, Analytics thought leader
 excerpt from The Wall Street Journal

 What are the differences between data mining, machine learning and deep learning?

 Although all of these methods have the same goal – to extract insights, patterns and relationships that can be used to make decisions – they have different approaches and abilities.

 Data Mining

 Data mining can be considered a superset of many different methods to extract insights from data. It might involve traditional statistical methods and machine learning. Data mining applies methods from many different areas to identify previously unknown patterns from data. This can include statistical algorithms, machine learning, text analytics, time series analysis and other areas of analytics. Data mining also includes the study and practice of data storage and data manipulation.


 Machine Learning

 The main difference with machine learning is that just like statistical models, the goal is to understand the structure of the data – fit theoretical distributions to the data that are well understood. So, with statistical models there is a theory behind the model that is mathematically proven, but this requires that data meets certain strong assumptions too. Machine learning has developed based on the ability to use computers to probe the data for structure, even if we do not have a theory of what that structure looks like. The test for a machine learning model is a validation error on new data, not a theoretical test that proves a null hypothesis. Because machine learning often uses an iterative approach to learn from data, the learning can be easily automated. Passes are run through the data until a robust pattern is found.


 Deep learning

 Deep learning combines advances in computing power and special types of neural networks to learn complicated patterns in large amounts of data. Deep learning techniques are currently state of the art for identifying objects in images and words in sounds. Researchers are now looking to apply these successes in pattern recognition to more complex tasks such as automatic language translation, medical diagnoses and numerous other important social and business problems.

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SAS Insights
	
	 
	
	
	
	 Analytics Insights 
	
	 
	
	 
 
 What it is and why it matters
  
 Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. 
 
Evolution of machine learning
Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.
While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:
 The heavily hyped, self-driving Google car? The essence of machine learning. Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. Fraud detection? One of the more obvious, important uses in our world today.
 
Machine Learning and Artificial Intelligence
While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Watch this video to better understand the relationship between AI and machine learning. You'll see how these two technologies work, with useful examples and a few funny asides.
  
 Read more about this topic
 
  
 
 
 
 
 
 
 
Analytics tackles the scourge of human traffickingVictims of human trafficking are all around us. From forced labor to sex work, modern-day slavery thrives in the shadows. Learn why organizations are turning to AI and big data analytics to unveil these crimes and change future trajectories.
  
 
 
 
 
 
Viking transforms its analytics strategy using SAS® Viya® on AzureViking is going all-in on cloud-based analytics to stay competitive and meet customer needs. The retailer's digital transformation are designed to optimize processes and boost customer loyalty and revenue across channels.
  
 
 
 
 
 
Public health infrastructure desperately needs modernizationPublic health agencies must flex to longitudinal health crises and acute emergencies – from natural disasters like hurricanes to events like a pandemic. To be prepared, public health infrastructure must be modernized to support connectivity, real-time data exchanges, analytics and visualization.
  
 
 
 
 
 
SAS CIO: Why leaders must cultivate curiosity in 2021With the change we’re all facing this year, CIOs should be counting on curiosity to play a crucial role in how we’re going to meet the challenges that lie ahead. From the moment COVID-19 hit, our IT organization has relied on curiosity – that strong desire to explore, learn, know - to fuel the urgent changes required. And it’s curiosity that will enable us to meet the needs of the future of work post-pandemic.
  
 
 
 
What are some popular machine learning methods?
Two of the most widely adopted machine learning methods are supervised learning and unsupervised learning – but there are also other methods of machine learning. Here's an overview of the most popular types.
 
 Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known. For example, a piece of equipment could have data points labeled either “F” (failed) or “R” (runs). The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. It then modifies the model accordingly. Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data. Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.
 Unsupervised learning is used against data that has no historical labels. The system is not told the "right answer." The algorithm must figure out what is being shown. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns. Or it can find the main attributes that separate customer segments from each other. Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition. These algorithms are also used to segment text topics, recommend items and identify data outliers.
 
 Semisupervised learning is used for the same applications as supervised learning. But it uses both labeled and unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data (because unlabeled data is less expensive and takes less effort to acquire). This type of learning can be used with methods such as classification, regression and prediction. Semisupervised learning is useful when the cost associated with labeling is too high to allow for a fully labeled training process. Early examples of this include identifying a person's face on a web cam.
 Reinforcement learning is often used for robotics, gaming and navigation. With reinforcement learning, the algorithm discovers through trial and error which actions yield the greatest rewards. This type of learning has three primary components: the agent (the learner or decision maker), the environment (everything the agent interacts with) and actions (what the agent can do). The objective is for the agent to choose actions that maximize the expected reward over a given amount of time. The agent will reach the goal much faster by following a good policy. So the goal in reinforcement learning is to learn the best policy.
  
Humans can typically create one or two good models a week; machine learning can create thousands of models a week.
  Thomas H. Davenport, Analytics thought leader
excerpt from The Wall Street Journal
Machine Learning: What it is and why it matters
https://www.sas.com/en_us/insights/analytics/machine-learning.html#:~:text=Machine%20learning%20is%20a%20method,decisions%20with%20minimal%20human%20intervention.
https://www.engadget.com/twitch-suspicious-user-detection-announcement-000510859.html
Twitch will use machine learning to catch ban-dodging trolls
2021-12-01
Igor Bonifacic
<div><div><p>Twitch is introducing a new machine learning feature to help streamers protect their channels from people attempting to avoid bans. Dubbed "Suspicious User Detection," the tool will automatically flag individuals it suspects may be "likely" or "possible" ban dodgers.</p><p>In cases involving the former, Twitch will prevent any messages they send from showing up in chat. It will also identify those individuals for streamers and any mods helping them with their channel. At that point, they can decide if they want to ban that person. By default, possible repeat trolls can send messages in chat, but they too will be flagged by the system. Additionally, Twitch says creators have the option to prevent them from sending any messages in the first place.</p><figure><div><p></p></div><p><figcaption></figcaption></p></figure><p>"The tool is powered by a machine learning model that takes a number of signals into account — including, but not limited to, the user's behavior and account characteristics — and compares that data against accounts previously banned from a Creator's channel to assess the likelihood the account is evading a previous channel-level ban," a Twitch spokesperson told Engadget when we asked about the signals the system uses to detect potential offenders.</p><p>While Twitch plans to turn on Suspicious User Detection for everyone, the tool won't automatically ban users for streamers. That's by design because it's impossible to create a machine learning tool that is 100 percent accurate in every context. "You're the expert when it comes to your community, and you should make the final call on who can participate," the company said in a <a href="https://safety.twitch.tv/s/safety-news/detail?language=en_US&amp;post=Using-Machine-Learning-to-Curb-Ban-Evasion"></a>. "The tool will learn from the actions you take and the accuracy of its predictions should improve over time as a result."</p><p>The introduction of the tool follows a summer in which Twitch struggled to contain a phenomenon called "hate raids." The attacks saw malicious individuals use thousands of bots to spam channels with hateful language. In many cases, they targeted creators from marginalized communities. Hate raids became such a frequent feature of the platform that some creators walked away from Twitch for a day in <a href="https://www.engadget.com/twitch-streamers-are-taking-a-day-off-next-week-to-protest-hate-raids-065515266.html"></a>company's<a href="https://www.engadget.com/twitch-streamers-are-taking-a-day-off-next-week-to-protest-hate-raids-065515266.html"></a>.</p></div></div>||||I|||| Engadget
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 Read full article

 Twitch will use machine learning to catch ban-dodging trolls

 The tool will prevent 'likely' culprits from taking part in chat.

 Igor Bonifacic
 Contributing Reporter
 Tue, Nov 30, 2021, 7:05 PM · 2 min read
 0

 Mike Blake / Reuters

 Twitch is introducing a new machine learning feature to help streamers protect their channels from people attempting to avoid bans. Dubbed "Suspicious User Detection," the tool will automatically flag individuals it suspects may be "likely" or "possible" ban dodgers.

 In cases involving the former, Twitch will prevent any messages they send from showing up in chat. It will also identify those individuals for streamers and any mods helping them with their channel. At that point, they can decide if they want to ban that person. By default, possible repeat trolls can send messages in chat, but they too will be flagged by the system. Additionally, Twitch says creators have the option to prevent them from sending any messages in the first place.

 Twitch

 "The tool is powered by a machine learning model that takes a number of signals into account — including, but not limited to, the user's behavior and account characteristics — and compares that data against accounts previously banned from a Creator's channel to assess the likelihood the account is evading a previous channel-level ban," a Twitch spokesperson told Engadget when we asked about the signals the system uses to detect potential offenders.

 While Twitch plans to turn on Suspicious User Detection for everyone, the tool won't automatically ban users for streamers. That's by design because it's impossible to create a machine learning tool that is 100 percent accurate in every context. "You're the expert when it comes to your community, and you should make the final call on who can participate," the company said in a blog post. "The tool will learn from the actions you take and the accuracy of its predictions should improve over time as a result."

 The introduction of the tool follows a summer in which Twitch struggled to contain a phenomenon called "hate raids." The attacks saw malicious individuals use thousands of bots to spam channels with hateful language. In many cases, they targeted creators from marginalized communities. Hate raids became such a frequent feature of the platform that some creators walked away from Twitch for a day in protest of the company's lack of action.

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Twitch is introducing a new machine learning feature to help streamers protect their channels from people attempting to avoid bans. Dubbed "Suspicious User Detection," the tool will automatically flag individuals it suspects may be "likely" or "possible" ban dodgers. In cases involving the former, Twitch will prevent any messages they send from showing up in chat. It will also identify those individuals for streamers and any mods helping them with their channel. At that point, they can decide if they want to ban that person. By default, possible repeat trolls can send messages in chat, but they too will be flagged by the system. Additionally, Twitch says creators have the option to prevent them from sending any messages in the first place.           "The tool is powered by a machine learning model that takes a number of signals into account — including, but not limited to, the user's behavior and account characteristics — and compares that data against accounts previously banned from a Creator's channel to assess the likelihood the account is evading a previous channel-level ban," a Twitch spokesperson told Engadget when we asked about the signals the system uses to detect potential offenders. While Twitch plans to turn on Suspicious User Detection for everyone, the tool won't automatically ban users for streamers. That's by design because it's impossible to create a machine learning tool that is 100 percent accurate in every context. "You're the expert when it comes to your community, and you should make the final call on who can participate," the company said in a  . "The tool will learn from the actions you take and the accuracy of its predictions should improve over time as a result." The introduction of the tool follows a summer in which Twitch struggled to contain a phenomenon called "hate raids." The attacks saw malicious individuals use thousands of bots to spam channels with hateful language. In many cases, they targeted creators from marginalized communities. Hate raids became such a frequent feature of the platform that some creators walked away from Twitch for a day in  company's .
Twitch will use machine learning to catch ban-dodging trolls
https://www.engadget.com/twitch-suspicious-user-detection-announcement-000510859.html
https://www.crn.com/slide-shows/applications-os/the-10-hottest-machine-learning-and-data-science-startups-in-2021
The 10 Hottest Machine Learning And Data Science Startups In 2021 | CRN
2021-11-29
Rick Whiting
<div><div>
 
	<a href="/slide-shows/">
	<h3> News <i></i> </h3>
	</a>
	 <a href="/authors/rick-whiting">
	</a>
	<h5><time>November 29, 2021, 01:17 PM EST</time></h5>
	<p>Businesses looking to bring the rewards of big data analysis to everyday users need ways to prepare and organize data and develop machine learning models for analyzing it. Here’s a look at 10 hot startups developing leading-edge data science and machine learning technologies that help them do that.</p>
	 
	 	
	<div>
	<figure>
	 
	<figcaption></figcaption>
	</figure>
	
<p>
 <b>A Scientific Approach</b>
</p>
<p>
 <b>Read the latest entry:</b> <a href="https://www.crn.com/news/applications-os/the-10-hottest-data-science-and-machine-learning-startups-in-2022">The 10 Hottest Data Science and Machine Learning Startups of 2022</a>
</p>
<p>Businesses today are struggling to leverage exploding volumes of data for competitive advantage. To meet that challenge, data engineers and data scientists are increasingly turning to emerging technologies in data science, artificial intelligence, machine learning and even deep learning to prepare and organize big data and develop the machine learning algorithms and predictive models that support business intelligence applications used by business analysts and information workers.</p>
<p>Here’s a look at 10 top data science and machine learning startup companies with leading-edge products in the data science and machine learning arena that solution providers should be aware of.</p>
	</div>
 
	 
	 	 
	 	 	 	 
	
<div>
	<p><a href="/authors/rick-whiting">
	</a>
	</p>
	<div>
	<a href="/authors/rick-whiting">
	<h5>Rick Whiting <i></i></h5>
	</a>
	<p>Rick Whiting has been with CRN since 2006 and is currently a feature/special projects editor. Whiting manages a number of CRN’s signature annual editorial projects including Channel Chiefs, Partner Program Guide, Big Data 100, Emerging Vendors, Tech Innovators and Products of the Year. He also covers the Big Data beat for CRN. He can be reached at <a href="mailto:rwhiting@thechannelcompany.com">rwhiting@thechannelcompany.com</a>.</p>
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 * Home ▸ Slide Shows ▸ The 10 Hottest Machine Learning And Data Science Startups In 2021

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 The 10 Hottest Machine Learning And Data Science Startups In 2021

 Rick Whiting

 November 29, 2021, 01:17 PM EST

 Businesses looking to bring the rewards of big data analysis to everyday users need ways to prepare and organize data and develop machine learning models for analyzing it. Here’s a look at 10 hot startups developing leading-edge data science and machine learning technologies that help them do that.

 * 1
 * 2
 * 3
 * ... 11
 * Next

 A Scientific Approach

 Read the latest entry: The 10 Hottest Data Science and Machine Learning Startups of 2022

 Businesses today are struggling to leverage exploding volumes of data for competitive advantage. To meet that challenge, data engineers and data scientists are increasingly turning to emerging technologies in data science, artificial intelligence, machine learning and even deep learning to prepare and organize big data and develop the machine learning algorithms and predictive models that support business intelligence applications used by business analysts and information workers.

 Here’s a look at 10 top data science and machine learning startup companies with leading-edge products in the data science and machine learning arena that solution providers should be aware of.

 * 1
 * 2
 * 3
 * ... 11
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 Learn More: Business Intelligence and Analytics

 Rick Whiting

 Rick Whiting has been with CRN since 2006 and is currently a feature/special projects editor. Whiting manages a number of CRN’s signature annual editorial projects including Channel Chiefs, Partner Program Guide, Big Data 100, Emerging Vendors, Tech Innovators and Products of the Year. He also covers the Big Data beat for CRN. He can be reached at rwhiting@thechannelcompany.com.

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News   
	
	 
	
	 November 29, 2021, 01:17 PM EST 
	Businesses looking to bring the rewards of big data analysis to everyday users need ways to prepare and organize data and develop machine learning models for analyzing it. Here’s a look at 10 hot startups developing leading-edge data science and machine learning technologies that help them do that.
	 
	 	
	
	
	 
	 
	
	
 A Scientific Approach
 Read the latest entry: The 10 Hottest Data Science and Machine Learning Startups of 2022
Businesses today are struggling to leverage exploding volumes of data for competitive advantage. To meet that challenge, data engineers and data scientists are increasingly turning to emerging technologies in data science, artificial intelligence, machine learning and even deep learning to prepare and organize big data and develop the machine learning algorithms and predictive models that support business intelligence applications used by business analysts and information workers.
Here’s a look at 10 top data science and machine learning startup companies with leading-edge products in the data science and machine learning arena that solution providers should be aware of.
	
 
	 
	 	 
	 	 	 	 
	
	 
	
	
	
	
	Rick Whiting   
	
	Rick Whiting has been with CRN since 2006 and is currently a feature/special projects editor. Whiting manages a number of CRN’s signature annual editorial projects including Channel Chiefs, Partner Program Guide, Big Data 100, Emerging Vendors, Tech Innovators and Products of the Year. He also covers the Big Data beat for CRN. He can be reached at rwhiting@thechannelcompany.com.
The 10 Hottest Machine Learning And Data Science Startups In 2021 | CRN
https://www.crn.com/slide-shows/applications-os/the-10-hottest-machine-learning-and-data-science-startups-in-2021
https://en.wikipedia.org/wiki/Machine_learning
Machine learning - Wikipedia
2003-05-25
Authority control National libraries Germany Israel United States Japan Czech Republic
<div><div>
<p><b>Machine learning</b> (<b>ML</b>) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks.<sup><a href="#cite_note-1">[1]</a></sup> It is seen as a part of <a href="/wiki/Artificial_intelligence">artificial intelligence</a>. 
</p><p>Machine learning algorithms build a model based on sample data, known as <a href="/wiki/Training_data">training data</a>, in order to make predictions or decisions without being explicitly programmed to do so.<sup><a href="#cite_note-2">[2]</a></sup> Machine learning algorithms are used in a wide variety of applications, such as in medicine, <a href="/wiki/Email_filtering">email filtering</a>, <a href="/wiki/Speech_recognition">speech recognition</a>, <a href="/wiki/Agriculture">agriculture</a>, and <a href="/wiki/Computer_vision">computer vision</a>, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.<sup><a href="#cite_note-tvt-3">[3]</a></sup><sup><a href="#cite_note-4">[4]</a></sup>
</p><p>A subset of machine learning is closely related to <a href="/wiki/Computational_statistics">computational statistics</a>, which focuses on making predictions using computers, but not all machine learning is statistical learning. The study of <a href="/wiki/Mathematical_optimization">mathematical optimization</a> delivers methods, theory and application domains to the field of machine learning. <a href="/wiki/Data_mining">Data mining</a> is a related field of study, focusing on <a href="/wiki/Exploratory_data_analysis">exploratory data analysis</a> through <a href="/wiki/Unsupervised_learning">unsupervised learning</a>.<sup><a href="#cite_note-6">[6]</a></sup><sup><a href="#cite_note-7">[7]</a></sup> 
</p><p>Some implementations of machine learning use data and <a href="/wiki/Neural_networks">neural networks</a> in a way that mimics the working of a <a href="/wiki/Brain">biological brain</a>.<sup><a href="#cite_note-8">[8]</a></sup><sup><a href="#cite_note-9">[9]</a></sup> 
</p><p>In its application across business problems, machine learning is also referred to as <a href="/wiki/Predictive_analytics">predictive analytics</a>.
</p>
<h2>Overview[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=1">edit</a>]</h2>
<p>Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". They can be nuanced, such as "X% of <a href="/wiki/Family_(biology)">families</a> have geographically separate species with color variants, so there is a Y% chance that undiscovered <a href="/wiki/Black_swan_theory">black swans</a> exist".<sup><a href="#cite_note-FOOTNOTEDomingos2015Chapter_6,_Chapter_7-10">[10]</a></sup>
</p><p>Machine learning programs can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than having human programmers specify every needed step.<sup><a href="#cite_note-Alpaydin2020-11">[11]</a></sup>
</p><p>The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available. In cases where vast numbers of potential answers exist, one approach is to label some of the correct answers as valid. This can then be used as training data for the computer to improve the algorithm(s) it uses to determine correct answers. For example, to train a system for the task of digital character recognition, the <a href="/wiki/MNIST_database">MNIST</a> dataset of handwritten digits has often been used.<sup><a href="#cite_note-Alpaydin2020-11">[11]</a></sup>
</p>
<h2>History and relationships to other fields[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=2">edit</a>]</h2>
<p>The term <i>machine learning</i> was coined in 1959 by <a href="/wiki/Arthur_Samuel_(computer_scientist)">Arthur Samuel</a>, an <a href="/wiki/IBM">IBM</a> employee and pioneer in the field of <a href="/wiki/Computer_gaming">computer gaming</a> and <a href="/wiki/Artificial_intelligence">artificial intelligence</a>.<sup><a href="#cite_note-Samuel-12">[12]</a></sup><sup><a href="#cite_note-13">[13]</a></sup> The synonym <i>self-teaching computers</i> was also used in this time period.<sup><a href="#cite_note-cyberthreat-14">[14]</a></sup><sup><a href="#cite_note-15">[15]</a></sup>
</p><p>By the early 1960s an experimental "learning machine" with <a href="/wiki/Punched_tape">punched tape</a> memory, called CyberTron, had been developed by <a href="/wiki/Raytheon_Company">Raytheon Company</a> to analyze sonar signals, electrocardiograms, and speech patterns using rudimentary <a href="/wiki/Reinforcement_learning">reinforcement learning</a>. It was repetitively "trained" by a human operator/teacher to recognize patterns and equipped with a "<a href="/wiki/Goof">goof</a>" button to cause it to re-evaluate incorrect decisions.<sup><a href="#cite_note-16">[16]</a></sup> A representative book on research into machine learning during the 1960s was Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification.<sup><a href="#cite_note-17">[17]</a></sup> Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973.<sup><a href="#cite_note-18">[18]</a></sup> In 1981 a report was given on using teaching strategies so that a <a href="/wiki/Neural_network">neural network</a> learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.<sup><a href="#cite_note-19">[19]</a></sup>
</p><p><a href="/wiki/Tom_M._Mitchell">Tom M. Mitchell</a> provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience <i>E</i> with respect to some class of tasks <i>T</i> and performance measure <i>P</i> if its performance at tasks in <i>T</i>, as measured by <i>P</i>, improves with experience <i>E</i>."<sup><a href="#cite_note-Mitchell-1997-20">[20]</a></sup> This definition of the tasks in which machine learning is concerned offers a fundamentally <a href="/wiki/Operational_definition">operational definition</a> rather than defining the field in cognitive terms. This follows <a href="/wiki/Alan_Turing">Alan Turing</a>'s proposal in his paper "<a href="/wiki/Computing_Machinery_and_Intelligence">Computing Machinery and Intelligence</a>", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".<sup><a href="#cite_note-21">[21]</a></sup>
</p><p>Modern-day machine learning has two objectives, one is to classify data based on models which have been developed, the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. A machine learning algorithm for stock trading may inform the trader of future potential predictions.<sup><a href="#cite_note-22">[22]</a></sup>
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<h3>Artificial intelligence[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=3">edit</a>]</h3>
<div><p><a href="/wiki/File:AI_hierarchy.svg"></a></p><div><p>Machine learning as subfield of AI<sup><a href="#cite_note-journalimcms.org-23">[23]</a></sup></p></div></div>
<div><p><a href="/wiki/File:Fig-y_Part_of_ML_as_subfield_of_AI_or_AI_as_subfield_of_ML.jpg"></a></p><div><p>Part of machine learning as subfield of AI or part of AI as subfield of machine learning<sup><a href="#cite_note-24">[24]</a></sup></p></div></div>
<p>As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. In the early days of AI as an <a href="/wiki/Discipline_(academia)">academic discipline</a>, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what was then termed "<a href="/wiki/Neural_network">neural networks</a>"; these were mostly <a href="/wiki/Perceptron">perceptrons</a> and <a href="/wiki/ADALINE">other models</a> that were later found to be reinventions of the <a href="/wiki/Generalized_linear_model">generalized linear models</a> of statistics.<sup><a href="#cite_note-25">[25]</a></sup> <a href="/wiki/Probabilistic_reasoning">Probabilistic reasoning</a> was also employed, especially in <a href="/wiki/Automated_medical_diagnosis">automated medical diagnosis</a>.<sup><a href="#cite_note-aima-26">[26]</a></sup><sup>: 488 </sup>
</p><p>However, an increasing emphasis on the <a href="/wiki/GOFAI">logical, knowledge-based approach</a> caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.<sup><a href="#cite_note-aima-26">[26]</a></sup><sup>: 488 </sup> By 1980, <a href="/wiki/Expert_system">expert systems</a> had come to dominate AI, and statistics was out of favor.<sup><a href="#cite_note-changing-27">[27]</a></sup> Work on symbolic/knowledge-based learning did continue within AI, leading to <a href="/wiki/Inductive_logic_programming">inductive logic programming</a>, but the more statistical line of research was now outside the field of AI proper, in <a href="/wiki/Pattern_recognition">pattern recognition</a> and <a href="/wiki/Information_retrieval">information retrieval</a>.<sup><a href="#cite_note-aima-26">[26]</a></sup><sup>: 708–710, 755 </sup> Neural networks research had been abandoned by AI and <a href="/wiki/Computer_science">computer science</a> around the same time. This line, too, was continued outside the AI/CS field, as "<a href="/wiki/Connectionism">connectionism</a>", by researchers from other disciplines including <a href="/wiki/John_Hopfield">Hopfield</a>, <a href="/wiki/David_Rumelhart">Rumelhart</a>, and <a href="/wiki/Geoff_Hinton">Hinton</a>. Their main success came in the mid-1980s with the reinvention of <a href="/wiki/Backpropagation">backpropagation</a>.<sup><a href="#cite_note-aima-26">[26]</a></sup><sup>: 25 </sup>
</p><p>Machine learning (ML), reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the <a href="/wiki/Symbolic_artificial_intelligence">symbolic approaches</a> it had inherited from AI, and toward methods and models borrowed from statistics, <a href="/wiki/Fuzzy_logic">fuzzy logic</a>, and <a href="/wiki/Probability_theory">probability theory</a>.<sup><a href="#cite_note-changing-27">[27]</a></sup>
</p><p>The difference between ML and AI is frequently misunderstood. ML learns and predicts based on passive observations, whereas AI implies an agent interacting with the environment to learn and take actions that maximize its chance of successfully achieving its goals.<sup><a href="#cite_note-:3-28">[28]</a></sup>
</p><p>As of 2020, many sources continue to assert that ML remains a subfield of AI.<sup><a href="#cite_note-:4-29">[29]</a></sup><sup><a href="#cite_note-islr-30">[30]</a></sup><sup><a href="#cite_note-changing-27">[27]</a></sup> Others have the view that not all ML is part of AI, but only an 'intelligent subset' of ML should be considered AI.<sup><a href="#cite_note-bishop2006-5">[5]</a></sup><sup><a href="#cite_note-:5-31">[31]</a></sup><sup><a href="#cite_note-alpaydin-32">[32]</a></sup>
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<h3>Data mining[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=4">edit</a>]</h3>
<p>Machine learning and <a href="/wiki/Data_mining">data mining</a> often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on <i>known</i> properties learned from the training data, <a href="/wiki/Data_mining">data mining</a> focuses on the <a href="/wiki/Discovery_(observation)">discovery</a> of (previously) <i>unknown</i> properties in the data (this is the analysis step of <a href="/wiki/Knowledge_discovery">knowledge discovery</a> in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, <a href="/wiki/ECML_PKDD">ECML PKDD</a> being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to <i>reproduce known</i> knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously <i>unknown</i> knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.
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<h3>Optimization[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=5">edit</a>]</h3>
<p>Machine learning also has intimate ties to <a href="/wiki/Mathematical_optimization">optimization</a>: many learning problems are formulated as minimization of some <a href="/wiki/Loss_function">loss function</a> on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples).<sup><a href="#cite_note-33">[33]</a></sup>
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<h3>Generalization[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=6">edit</a>]</h3>
<p>The difference between optimization and machine learning arises from the goal of <a href="/wiki/Generalization_(learning)">generalization</a>: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples. Characterizing the generalization of various learning algorithms is an active topic of current research, especially for <a href="/wiki/Deep_learning">deep learning</a> algorithms.
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<h3>Statistics[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=7">edit</a>]</h3>
<p>Machine learning and <a href="/wiki/Statistics">statistics</a> are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population <a href="/wiki/Statistical_inference">inferences</a> from a <a href="/wiki/Sample_(statistics)">sample</a>, while machine learning finds generalizable predictive patterns.<sup><a href="#cite_note-34">[34]</a></sup> According to <a href="/wiki/Michael_I._Jordan">Michael I. Jordan</a>, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.<sup><a href="#cite_note-mi_jordan_ama-35">[35]</a></sup> He also suggested the term <a href="/wiki/Data_science">data science</a> as a placeholder to call the overall field.<sup><a href="#cite_note-mi_jordan_ama-35">[35]</a></sup>
</p><p><a href="/wiki/Leo_Breiman">Leo Breiman</a> distinguished two statistical modeling paradigms: data model and algorithmic model,<sup><a href="#cite_note-:4-29">[29]</a></sup> wherein "algorithmic model" means more or less the machine learning algorithms like <a href="/wiki/Random_forest">Random Forest</a>.
</p><p>Some statisticians have adopted methods from machine learning, leading to a combined field that they call <i>statistical learning</i>.<sup><a href="#cite_note-islr-30">[30]</a></sup>
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<h3>Physics[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=8">edit</a>]</h3>
<p>Analytical and computational techniques derived from statistical physics of disordered systems, can be extended to large-scale problems, including machine learning, e.g., to analyze the weight space of deep <a href="/wiki/Neural_networks">neural networks</a>.<sup><a href="#cite_note-36">[36]</a></sup> Statistical physics is thus finding applications in the area of <a href="/wiki/Medical_diagnostics">medical diagnostics</a>.<sup><a href="#cite_note-37">[37]</a></sup>
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<h2> Theory[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=9">edit</a>]</h2>
<p>A core objective of a learner is to generalize from its experience.<sup><a href="#cite_note-bishop2006-5">[5]</a></sup><sup><a href="#cite_note-:5-31">[31]</a></sup> Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.
</p><p>The computational analysis of machine learning algorithms and their performance is a branch of <a href="/wiki/Theoretical_computer_science">theoretical computer science</a> known as <a href="/wiki/Computational_learning_theory">computational learning theory</a> via the <a href="/wiki/Probably_approximately_correct_learning">Probably Approximately Correct Learning</a> (PAC) model. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The <a href="/wiki/Bias%E2%80%93variance_decomposition">bias–variance decomposition</a> is one way to quantify generalization <a href="/wiki/Errors_and_residuals">error</a>.
</p><p>For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to <a href="/wiki/Overfitting">overfitting</a> and generalization will be poorer.<sup><a href="#cite_note-alpaydin-32">[32]</a></sup>
</p><p>In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in <a href="/wiki/Time_complexity#Polynomial_time">polynomial time</a>. There are two kinds of <a href="/wiki/Time_complexity">time complexity</a> results: Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.
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<h2>Approaches[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=10">edit</a>]</h2>
<p>
Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on the nature of the "signal" or "feedback" available to the learning system:
</p>
<ul><li><a href="/wiki/Supervised_learning">Supervised learning</a>: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that <a href="/wiki/Map_(mathematics)">maps</a> inputs to outputs.</li>
<li><a href="/wiki/Unsupervised_learning">Unsupervised learning</a>: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (<a href="/wiki/Feature_learning">feature learning</a>).</li>
<li><a href="/wiki/Reinforcement_learning">Reinforcement learning</a>: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as <a href="/wiki/Autonomous_car">driving a vehicle</a> or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize.<sup><a href="#cite_note-bishop2006-5">[5]</a></sup></li></ul>
<h3>Supervised learning[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=11">edit</a>]</h3>
<div><p><a href="/wiki/File:Svm_max_sep_hyperplane_with_margin.png"></a></p><div><p>A <a href="/wiki/Support-vector_machine">support-vector machine</a> is a supervised learning model that divides the data into regions separated by a <a href="/wiki/Linear_classifier">linear boundary</a>. Here, the linear boundary divides the black circles from the white.</p></div></div>
<p>Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.<sup><a href="#cite_note-38">[38]</a></sup> The data is known as <a href="/wiki/Training_data">training data</a>, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an <a href="/wiki/Array_data_structure">array</a> or vector, sometimes called a feature vector, and the training data is represented by a <a href="/wiki/Matrix_(mathematics)">matrix</a>. Through <a href="/wiki/Mathematical_optimization#Computational_optimization_techniques">iterative optimization</a> of an <a href="/wiki/Loss_function">objective function</a>, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs.<sup><a href="#cite_note-39">[39]</a></sup> An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.<sup><a href="#cite_note-Mitchell-1997-20">[20]</a></sup>
</p><p>Types of supervised-learning algorithms include <a href="/wiki/Active_learning_(machine_learning)">active learning</a>, <a href="/wiki/Statistical_classification">classification</a> and <a href="/wiki/Regression_analysis">regression</a>.<sup><a href="#cite_note-:3-28">[28]</a></sup> Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email.
</p><p><a href="/wiki/Similarity_learning">Similarity learning</a> is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in <a href="/wiki/Ranking">ranking</a>, <a href="/wiki/Recommender_system">recommendation systems</a>, visual identity tracking, face verification, and speaker verification.
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<h3>Unsupervised learning[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=12">edit</a>]</h3>
<p>Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of <a href="/wiki/Density_estimation">density estimation</a> in <a href="/wiki/Statistics">statistics</a>, such as finding the <a href="/wiki/Probability_density_function">probability density function</a>.<sup><a href="#cite_note-JordanBishop2004-40">[40]</a></sup> Though unsupervised learning encompasses other domains involving summarizing and explaining data features.
</p><p>Cluster analysis is the assignment of a set of observations into subsets (called <i>clusters</i>) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some <i>similarity metric</i> and evaluated, for example, by <i>internal compactness</i>, or the similarity between members of the same cluster, and <i>separation</i>, the difference between clusters. Other methods are based on <i>estimated density</i> and <i>graph connectivity</i>.
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<h3>Semi-supervised learning[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=13">edit</a>]</h3>
<p>Semi-supervised learning falls between <a href="/wiki/Unsupervised_learning">unsupervised learning</a> (without any labeled training data) and <a href="/wiki/Supervised_learning">supervised learning</a> (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy.
</p><p>In <a href="/wiki/Weak_supervision">weakly supervised learning</a>, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets.<sup><a href="#cite_note-41">[41]</a></sup>
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<h3>Reinforcement learning[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=14">edit</a>]</h3>
<p>Reinforcement learning is an area of machine learning concerned with how <a href="/wiki/Software_agent">software agents</a> ought to take <a href="/wiki/Action_selection">actions</a> in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as <a href="/wiki/Game_theory">game theory</a>, <a href="/wiki/Control_theory">control theory</a>, <a href="/wiki/Operations_research">operations research</a>, <a href="/wiki/Information_theory">information theory</a>, <a href="/wiki/Simulation-based_optimization">simulation-based optimization</a>, <a href="/wiki/Multi-agent_system">multi-agent systems</a>, <a href="/wiki/Swarm_intelligence">swarm intelligence</a>, <a href="/wiki/Statistics">statistics</a> and <a href="/wiki/Genetic_algorithm">genetic algorithms</a>. In machine learning, the environment is typically represented as a <a href="/wiki/Markov_decision_process">Markov decision process</a> (MDP). Many reinforcements learning algorithms use <a href="/wiki/Dynamic_programming">dynamic programming</a> techniques.<sup><a href="#cite_note-42">[42]</a></sup> Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent.
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<h3>Dimensionality reduction[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=15">edit</a>]</h3>
<p>Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables.<sup><a href="#cite_note-43">[43]</a></sup> In other words, it is a process of reducing the dimension of the feature set, also called the "number of features". Most of the dimensionality reduction techniques can be considered as either feature elimination or extraction. One of the popular methods of dimensionality reduction is <a href="/wiki/Principal_component_analysis">principal component analysis</a> (PCA). PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D). This results in a smaller dimension of data (2D instead of 3D), while keeping all original variables in the model without changing the data.<sup><a href="#cite_note-44">[44]</a></sup>
The <a href="/wiki/Manifold_hypothesis">manifold hypothesis</a> proposes that high-dimensional data sets lie along low-dimensional <a href="/wiki/Manifold">manifolds</a>, and many dimensionality reduction techniques make this assumption, leading to the area of <a href="/wiki/Manifold_learning">manifold learning</a> and <a href="/wiki/Manifold_regularization">manifold regularization</a>.
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<h3>Other types[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=16">edit</a>]</h3>
<p>Other approaches have been developed which don't fit neatly into this three-fold categorization, and sometimes more than one is used by the same machine learning system. For example, <a href="/wiki/Topic_modeling">topic modeling</a>, <a href="/wiki/Meta-learning_(computer_science)">meta-learning</a>.<sup><a href="#cite_note-45">[45]</a></sup>
</p><p>As of 2022, <a href="/wiki/Deep_learning">deep learning</a> is the dominant approach for much ongoing work in the field of machine learning.<sup><a href="#cite_note-Alpaydin2020-11">[11]</a></sup>
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<h4>Self-learning[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=17">edit</a>]</h4>
<p>Self-learning, as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning, named <i>crossbar adaptive array</i> (CAA).<sup><a href="#cite_note-46">[46]</a></sup> It is learning with no external rewards and no external teacher advice. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion.<sup><a href="#cite_note-47">[47]</a></sup>
The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine: 
</p>
<ol><li>in situation <i>s</i> perform action <i>a</i></li>
<li>receive consequence situation <i>s'</i></li>
<li>compute emotion of being in consequence situation <i>v(s')</i></li>
<li>update crossbar memory <i>w'(a,s) = w(a,s) + v(s')</i></li></ol>
<p>It is a system with only one input, situation, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is the behavioral environment where it behaves, and the other is the genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal-seeking behavior, in an environment that contains both desirable and undesirable situations.<sup><a href="#cite_note-48">[48]</a></sup>
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<h4>Feature learning[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=18">edit</a>]</h4>
<p>Several learning algorithms aim at discovering better representations of the inputs provided during training.<sup><a href="#cite_note-pami-49">[49]</a></sup> Classic examples include <a href="/wiki/Principal_component_analysis">principal component analysis</a> and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual <a href="/wiki/Feature_engineering">feature engineering</a>, and allows a machine to both learn the features and use them to perform a specific task.
</p><p>Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include <a href="/wiki/Artificial_neural_network">artificial neural networks</a>, <a href="/wiki/Multilayer_perceptron">multilayer perceptrons</a>, and supervised <a href="/wiki/Dictionary_learning">dictionary learning</a>. In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, <a href="/wiki/Independent_component_analysis">independent component analysis</a>, <a href="/wiki/Autoencoder">autoencoders</a>, <a href="/wiki/Matrix_decomposition">matrix factorization</a><sup><a href="#cite_note-50">[50]</a></sup> and various forms of <a href="/wiki/Cluster_analysis">clustering</a>.<sup><a href="#cite_note-coates2011-51">[51]</a></sup><sup><a href="#cite_note-52">[52]</a></sup><sup><a href="#cite_note-jurafsky-53">[53]</a></sup>
</p><p><a href="/wiki/Manifold_learning">Manifold learning</a> algorithms attempt to do so under the constraint that the learned representation is low-dimensional. <a href="/wiki/Sparse_coding">Sparse coding</a> algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. <a href="/wiki/Multilinear_subspace_learning">Multilinear subspace learning</a> algorithms aim to learn low-dimensional representations directly from <a href="/wiki/Tensor">tensor</a> representations for multidimensional data, without reshaping them into higher-dimensional vectors.<sup><a href="#cite_note-54">[54]</a></sup> <a href="/wiki/Deep_learning">Deep learning</a> algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.<sup><a href="#cite_note-55">[55]</a></sup>
</p><p>Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.
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<h4>Sparse dictionary learning[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=19">edit</a>]</h4>
<p>Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of <a href="/wiki/Basis_function">basis functions</a>, and is assumed to be a <a href="/wiki/Sparse_matrix">sparse matrix</a>. The method is <a href="/wiki/Strongly_NP-hard">strongly NP-hard</a> and difficult to solve approximately.<sup><a href="#cite_note-56">[56]</a></sup> A popular <a href="/wiki/Heuristic">heuristic</a> method for sparse dictionary learning is the <a href="/wiki/K-SVD">K-SVD</a> algorithm. Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine the class to which a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in <a href="/wiki/Image_de-noising">image de-noising</a>. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot.<sup><a href="#cite_note-57">[57]</a></sup>
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<h4>Anomaly detection[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=20">edit</a>]</h4>
<p>In <a href="/wiki/Data_mining">data mining</a>, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.<sup><a href="#cite_note-:0-58">[58]</a></sup> Typically, the anomalous items represent an issue such as <a href="/wiki/Bank_fraud">bank fraud</a>, a structural defect, medical problems or errors in a text. Anomalies are referred to as <a href="/wiki/Outlier">outliers</a>, novelties, noise, deviations and exceptions.<sup><a href="#cite_note-59">[59]</a></sup>
</p><p>In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts of inactivity. This pattern does not adhere to the common statistical definition of an outlier as a rare object. Many outlier detection methods (in particular, unsupervised algorithms) will fail on such data unless aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns.<sup><a href="#cite_note-60">[60]</a></sup>
</p><p>Three broad categories of anomaly detection techniques exist.<sup><a href="#cite_note-ChandolaSurvey-61">[61]</a></sup> Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit the least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set and then test the likelihood of a test instance to be generated by the model.
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<h4>Robot learning[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=21">edit</a>]</h4>
<p><a href="/wiki/Robot_learning">Robot learning</a> is inspired by a multitude of machine learning methods, starting from supervised learning, reinforcement learning,<sup><a href="#cite_note-62">[62]</a></sup><sup><a href="#cite_note-63">[63]</a></sup> and finally <a href="/wiki/Meta-learning_(computer_science)">meta-learning</a> (e.g. MAML).
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<h4>Association rules[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=22">edit</a>]</h4>
<p>Association rule learning is a <a href="/wiki/Rule-based_machine_learning">rule-based machine learning</a> method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".<sup><a href="#cite_note-piatetsky-64">[64]</a></sup>
</p><p>Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.<sup><a href="#cite_note-65">[65]</a></sup> Rule-based machine learning approaches include <a href="/wiki/Learning_classifier_system">learning classifier systems</a>, association rule learning, and <a href="/wiki/Artificial_immune_system">artificial immune systems</a>.
</p><p>Based on the concept of strong rules, <a href="/wiki/Rakesh_Agrawal_(computer_scientist)">Rakesh Agrawal</a>, <a href="/wiki/Tomasz_Imieli%C5%84ski">Tomasz Imieliński</a> and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by <a href="/wiki/Point-of-sale">point-of-sale</a> (POS) systems in supermarkets.<sup><a href="#cite_note-mining-66">[66]</a></sup> For example, the rule found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional <a href="/wiki/Pricing">pricing</a> or <a href="/wiki/Product_placement">product placements</a>. In addition to <a href="/wiki/Market_basket_analysis">market basket analysis</a>, association rules are employed today in application areas including <a href="/wiki/Web_usage_mining">Web usage mining</a>, <a href="/wiki/Intrusion_detection">intrusion detection</a>, <a href="/wiki/Continuous_production">continuous production</a>, and <a href="/wiki/Bioinformatics">bioinformatics</a>. In contrast with <a href="/wiki/Sequence_mining">sequence mining</a>, association rule learning typically does not consider the order of items either within a transaction or across transactions.
</p><p>Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a <a href="/wiki/Genetic_algorithm">genetic algorithm</a>, with a learning component, performing either <a href="/wiki/Supervised_learning">supervised learning</a>, <a href="/wiki/Reinforcement_learning">reinforcement learning</a>, or <a href="/wiki/Unsupervised_learning">unsupervised learning</a>. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a <a href="/wiki/Piecewise">piecewise</a> manner in order to make predictions.<sup><a href="#cite_note-67">[67]</a></sup>
</p><p>Inductive logic programming (ILP) is an approach to rule learning using <a href="/wiki/Logic_programming">logic programming</a> as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that <a href="/wiki/Entailment">entails</a> all positive and no negative examples. <a href="/wiki/Inductive_programming">Inductive programming</a> is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as <a href="/wiki/Functional_programming">functional programs</a>.
</p><p>Inductive logic programming is particularly useful in <a href="/wiki/Bioinformatics">bioinformatics</a> and <a href="/wiki/Natural_language_processing">natural language processing</a>. <a href="/wiki/Gordon_Plotkin">Gordon Plotkin</a> and <a href="/wiki/Ehud_Shapiro">Ehud Shapiro</a> laid the initial theoretical foundation for inductive machine learning in a logical setting.<sup><a href="#cite_note-68">[68]</a></sup><sup><a href="#cite_note-69">[69]</a></sup><sup><a href="#cite_note-70">[70]</a></sup> Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.<sup><a href="#cite_note-71">[71]</a></sup> The term <i>inductive</i> here refers to <a href="/wiki/Inductive_reasoning">philosophical</a> induction, suggesting a theory to explain observed facts, rather than <a href="/wiki/Mathematical_induction">mathematical induction</a>, proving a property for all members of a well-ordered set.
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<h3>Models[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=23">edit</a>]</h3>
<p>Performing machine learning involves creating a <a href="/wiki/Statistical_model">model</a>, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems.
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<h4>Artificial neural networks[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=24">edit</a>]</h4>
<div><p><a href="/wiki/File:Colored_neural_network.svg"></a></p><div><p>An artificial neural network is an interconnected group of nodes, akin to the vast network of <a href="/wiki/Neuron">neurons</a> in a <a href="/wiki/Brain">brain</a>. Here, each circular node represents an <a href="/wiki/Artificial_neuron">artificial neuron</a> and an arrow represents a connection from the output of one artificial neuron to the input of another.</p></div></div>
<p>Artificial neural networks (ANNs), or <a href="/wiki/Connectionism">connectionist</a> systems, are computing systems vaguely inspired by the <a href="/wiki/Biological_neural_network">biological neural networks</a> that constitute animal <a href="/wiki/Brain">brains</a>. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.
</p><p>An ANN is a model based on a collection of connected units or nodes called "<a href="/wiki/Artificial_neuron">artificial neurons</a>", which loosely model the <a href="/wiki/Neuron">neurons</a> in a biological <a href="/wiki/Brain">brain</a>. Each connection, like the <a href="/wiki/Synapse">synapses</a> in a biological <a href="/wiki/Brain">brain</a>, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a <a href="/wiki/Real_number">real number</a>, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a <a href="/wiki/Weight_(mathematics)">weight</a> that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times.
</p><p>The original goal of the ANN approach was to solve problems in the same way that a <a href="/wiki/Human_brain">human brain</a> would. However, over time, attention moved to performing specific tasks, leading to deviations from <a href="/wiki/Biology">biology</a>. Artificial neural networks have been used on a variety of tasks, including <a href="/wiki/Computer_vision">computer vision</a>, <a href="/wiki/Speech_recognition">speech recognition</a>, <a href="/wiki/Machine_translation">machine translation</a>, <a href="/wiki/Social_network">social network</a> filtering, <a href="/wiki/General_game_playing">playing board and video games</a> and <a href="/wiki/Medical_diagnosis">medical diagnosis</a>.
</p><p><a href="/wiki/Deep_learning">Deep learning</a> consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are <a href="/wiki/Computer_vision">computer vision</a> and <a href="/wiki/Speech_recognition">speech recognition</a>.<sup><a href="#cite_note-72">[72]</a></sup>
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<h4>Decision trees[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=25">edit</a>]</h4>
<div><p><a href="/wiki/File:Decision_Tree.jpg"></a></p><div><p>A decision tree showing survival probability of passengers on the <a href="/wiki/Titanic">Titanic</a></p></div></div>
<p>Decision tree learning uses a <a href="/wiki/Decision_tree">decision tree</a> as a <a href="/wiki/Predictive_modeling">predictive model</a> to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, <a href="/wiki/Leaf_node">leaves</a> represent class labels, and branches represent <a href="/wiki/Logical_conjunction">conjunctions</a> of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically <a href="/wiki/Real_numbers">real numbers</a>) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and <a href="/wiki/Decision_making">decision making</a>. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision-making.
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<h4>Support-vector machines[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=26">edit</a>]</h4>
<p>Support-vector machines (SVMs), also known as support-vector networks, are a set of related <a href="/wiki/Supervised_learning">supervised learning</a> methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category.<sup><a href="#cite_note-CorinnaCortes-73">[73]</a></sup> An SVM training algorithm is a non-<a href="/wiki/Probabilistic_classification">probabilistic</a>, <a href="/wiki/Binary_classifier">binary</a>, <a href="/wiki/Linear_classifier">linear classifier</a>, although methods such as <a href="/wiki/Platt_scaling">Platt scaling</a> exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the <a href="/wiki/Kernel_trick">kernel trick</a>, implicitly mapping their inputs into high-dimensional feature spaces.
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<h4>Regression analysis[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=27">edit</a>]</h4>
<div><p><a href="/wiki/File:Linear_regression.svg"></a></p><div><p>Illustration of linear regression on a data set</p></div></div>
<p>Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is <a href="/wiki/Linear_regression">linear regression</a>, where a single line is drawn to best fit the given data according to a mathematical criterion such as <a href="/wiki/Ordinary_least_squares">ordinary least squares</a>. The latter is often extended by <a href="/wiki/Regularization_(mathematics)">regularization</a> methods to mitigate overfitting and bias, as in <a href="/wiki/Ridge_regression">ridge regression</a>. When dealing with non-linear problems, go-to models include <a href="/wiki/Polynomial_regression">polynomial regression</a> (for example, used for trendline fitting in Microsoft Excel<sup><a href="#cite_note-74">[74]</a></sup>), <a href="/wiki/Logistic_regression">logistic regression</a> (often used in <a href="/wiki/Statistical_classification">statistical classification</a>) or even <a href="/wiki/Kernel_regression">kernel regression</a>, which introduces non-linearity by taking advantage of the <a href="/wiki/Kernel_trick">kernel trick</a> to implicitly map input variables to higher-dimensional space.
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<h4>Bayesian networks[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=28">edit</a>]</h4>
<div><p><a href="/wiki/File:SimpleBayesNetNodes.svg"></a></p><div><p>A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet.</p></div></div>
<p>A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic <a href="/wiki/Graphical_model">graphical model</a> that represents a set of <a href="/wiki/Random_variables">random variables</a> and their <a href="/wiki/Conditional_independence">conditional independence</a> with a <a href="/wiki/Directed_acyclic_graph">directed acyclic graph</a> (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform <a href="/wiki/Inference">inference</a> and learning. Bayesian networks that model sequences of variables, like <a href="/wiki/Speech_recognition">speech signals</a> or <a href="/wiki/Peptide_sequence">protein sequences</a>, are called <a href="/wiki/Dynamic_Bayesian_network">dynamic Bayesian networks</a>. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called <a href="/wiki/Influence_diagram">influence diagrams</a>.
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<h4>Gaussian processes[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=29">edit</a>]</h4>
<div><p><a href="/wiki/File:Regressions_sine_demo.svg"></a></p><div><p>An example of Gaussian Process Regression (prediction) compared with other regression models<sup><a href="#cite_note-75">[75]</a></sup></p></div></div>
<p>A Gaussian process is a <a href="/wiki/Stochastic_process">stochastic process</a> in which every finite collection of the random variables in the process has a <a href="/wiki/Multivariate_normal_distribution">multivariate normal distribution</a>, and it relies on a pre-defined <a href="/wiki/Covariance_function">covariance function</a>, or kernel, that models how pairs of points relate to each other depending on their locations.
</p><p>Given a set of observed points, or input–output examples, the distribution of the (unobserved) output of a new point as function of its input data, can be directly computed by looking like the observed points and the covariances between those points and the new, unobserved point.
</p><p>Gaussian processes are popular surrogate models in <a href="/wiki/Bayesian_optimization">Bayesian optimization</a> used to do <a href="/wiki/Hyperparameter_optimization">hyperparameter optimization</a>.
</p>
<h4>Genetic algorithms[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=30">edit</a>]</h4>
<p>A genetic algorithm (GA) is a <a href="/wiki/Search_algorithm">search algorithm</a> and <a href="/wiki/Heuristic_(computer_science)">heuristic</a> technique that mimics the process of <a href="/wiki/Natural_selection">natural selection</a>, using methods such as <a href="/wiki/Mutation_(genetic_algorithm)">mutation</a> and <a href="/wiki/Crossover_(genetic_algorithm)">crossover</a> to generate new <a href="/wiki/Chromosome_(genetic_algorithm)">genotypes</a> in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.<sup><a href="#cite_note-76">[76]</a></sup><sup><a href="#cite_note-77">[77]</a></sup> Conversely, machine learning techniques have been used to improve the performance of genetic and <a href="/wiki/Evolutionary_algorithm">evolutionary algorithms</a>.<sup><a href="#cite_note-78">[78]</a></sup>
</p>
<h3>Training models[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=31">edit</a>]</h3>
<p>Typically, machine learning models require a high quantity of reliable data in order for the models to perform accurate predictions. When training a machine learning model, machine learning engineers need to target and collect a large and representative sample of data. Data from the training set can be as varied as a corpus of text, a collection of images, sensor data, and data collected from individual users of a service. <a href="/wiki/Overfitting">Overfitting</a> is something to watch out for when training a machine learning model. Trained models derived from biased or non-evaluated data can result in skewed or undesired predictions. Bias models may result in detrimental outcomes thereby furthering the negative impacts on society or objectives. <a href="/wiki/Algorithmic_bias">Algorithmic bias</a> is a potential result of data not being fully prepared for training. Machine learning ethics is becoming a field of study and notably be integrated within machine learning engineering teams.
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<h4>Federated learning[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=32">edit</a>]</h4>
<p>Federated learning is an adapted form of <a href="/wiki/Distributed_artificial_intelligence">distributed artificial intelligence</a> to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, <a href="/wiki/Gboard">Gboard</a> uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to <a href="/wiki/Google">Google</a>.<sup><a href="#cite_note-79">[79]</a></sup>
</p>
<h2>Applications[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=33">edit</a>]</h2>
<p>There are many applications for machine learning, including:
</p>
<div>
<ul><li><a href="/wiki/Precision_agriculture">Agriculture</a></li>
<li><a href="/wiki/Computational_anatomy">Anatomy</a></li>
<li><a href="/wiki/Adaptive_website">Adaptive website</a></li>
<li><a href="/wiki/Affective_computing">Affective computing</a></li>
<li><a href="/wiki/Astroinformatics">Astronomy</a></li>
<li><a href="/wiki/Automated_decision-making">Automated decision-making</a></li>
<li><a href="/wiki/Banking">Banking</a></li>
<li><a href="/wiki/Behaviorism">Behaviorism</a></li>
<li><a href="/wiki/Bioinformatics">Bioinformatics</a></li>
<li><a href="/wiki/Brain%E2%80%93computer_interface">Brain–machine interfaces</a></li>
<li><a href="/wiki/Cheminformatics">Cheminformatics</a></li>
<li><a href="/wiki/Citizen_Science">Citizen Science</a></li>
<li><a href="/wiki/Climate_Science">Climate Science</a></li>
<li><a href="/wiki/Network_simulation">Computer networks</a></li>
<li><a href="/wiki/Computer_vision">Computer vision</a></li>
<li><a href="/wiki/Credit-card_fraud">Credit-card fraud</a> detection</li>
<li><a href="/wiki/Data_quality">Data quality</a></li>
<li><a href="/wiki/DNA_sequence">DNA sequence</a> classification</li>
<li><a href="/wiki/Computational_economics">Economics</a></li>
<li><a href="/wiki/Financial_market">Financial market</a> analysis<sup><a href="#cite_note-80">[80]</a></sup></li>
<li><a href="/wiki/General_game_playing">General game playing</a></li>
<li><a href="/wiki/Handwriting_recognition">Handwriting recognition</a></li>
<li><a href="/wiki/Information_retrieval">Information retrieval</a></li>
<li><a href="/wiki/Insurance">Insurance</a></li>
<li><a href="/wiki/Internet_fraud">Internet fraud</a> detection</li>
<li><a href="/wiki/Knowledge_graph_embedding">Knowledge graph embedding</a></li>
<li><a href="/wiki/Computational_linguistics">Linguistics</a></li>
<li><a href="/wiki/Machine_learning_control">Machine learning control</a></li>
<li><a href="/wiki/Machine_perception">Machine perception</a></li>
<li><a href="/wiki/Machine_translation">Machine translation</a></li>
<li><a href="/wiki/Marketing">Marketing</a></li>
<li><a href="/wiki/Automated_medical_diagnosis">Medical diagnosis</a></li>
<li><a href="/wiki/Natural_language_processing">Natural language processing</a></li>
<li><a href="/wiki/Natural-language_understanding">Natural language understanding</a></li>
<li><a href="/wiki/Online_advertising">Online advertising</a></li>
<li><a href="/wiki/Mathematical_optimization">Optimization</a></li>
<li><a href="/wiki/Recommender_system">Recommender systems</a></li>
<li><a href="/wiki/Robot_locomotion">Robot locomotion</a></li>
<li><a href="/wiki/Search_engines">Search engines</a></li>
<li><a href="/wiki/Sentiment_analysis">Sentiment analysis</a></li>
<li><a href="/wiki/Sequence_mining">Sequence mining</a></li>
<li><a href="/wiki/Software_engineering">Software engineering</a></li>
<li><a href="/wiki/Speech_recognition">Speech recognition</a></li>
<li><a href="/wiki/Structural_health_monitoring">Structural health monitoring</a></li>
<li><a href="/wiki/Syntactic_pattern_recognition">Syntactic pattern recognition</a></li>
<li><a href="/wiki/Telecommunication">Telecommunication</a></li>
<li><a href="/wiki/Automated_theorem_proving">Theorem proving</a></li>
<li><a href="/wiki/Time_series">Time-series forecasting</a></li>
<li><a href="/wiki/User_behavior_analytics">User behavior analytics</a></li></ul>
</div>
<p>In 2006, the media-services provider <a href="/wiki/Netflix">Netflix</a> held the first "<a href="/wiki/Netflix_Prize">Netflix Prize</a>" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from <a href="/wiki/AT%26T_Labs">AT&amp;T Labs</a>-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an <a href="/wiki/Ensemble_Averaging">ensemble model</a> to win the Grand Prize in 2009 for $1 million.<sup><a href="#cite_note-81">[81]</a></sup> Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.<sup><a href="#cite_note-82">[82]</a></sup> In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.<sup><a href="#cite_note-83">[83]</a></sup> In 2012, co-founder of <a href="/wiki/Sun_Microsystems">Sun Microsystems</a>, <a href="/wiki/Vinod_Khosla">Vinod Khosla</a>, predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software.<sup><a href="#cite_note-84">[84]</a></sup> In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.<sup><a href="#cite_note-85">[85]</a></sup> In 2019 <a href="/wiki/Springer_Nature">Springer Nature</a> published the first research book created using machine learning.<sup><a href="#cite_note-86">[86]</a></sup> In 2020, machine learning technology was used to help make diagnoses and aid researchers in developing a cure for COVID-19.<sup><a href="#cite_note-87">[87]</a></sup> Machine learning was recently applied to predict the pro-environmental behavior of travelers.<sup><a href="#cite_note-88">[88]</a></sup> Recently, machine learning technology was also applied to optimize smartphone's performance and thermal behavior based on the user's interaction with the phone.<sup><a href="#cite_note-89">[89]</a></sup><sup><a href="#cite_note-90">[90]</a></sup><sup><a href="#cite_note-91">[91]</a></sup>
</p>
<h2>Limitations[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=34">edit</a>]</h2>
<p>Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.<sup><a href="#cite_note-92">[92]</a></sup><sup><a href="#cite_note-93">[93]</a></sup><sup><a href="#cite_note-94">[94]</a></sup> Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.<sup><a href="#cite_note-95">[95]</a></sup>
</p><p>In 2018, a self-driving car from <a href="/wiki/Uber">Uber</a> failed to detect a pedestrian, who was killed after a collision.<sup><a href="#cite_note-96">[96]</a></sup> Attempts to use machine learning in healthcare with the <a href="/wiki/Watson_(computer)">IBM Watson</a> system failed to deliver even after years of time and billions of dollars invested.<sup><a href="#cite_note-97">[97]</a></sup><sup><a href="#cite_note-98">[98]</a></sup>
</p><p>Machine learning has been used as a strategy to update the evidence related to a systematic review and increased reviewer burden related to the growth of biomedical literature. While it has improved with training sets, it has not yet developed sufficiently to reduce the workload burden without limiting the necessary sensitivity for the findings research themselves.<sup><a href="#cite_note-99">[99]</a></sup>
</p>
<h3>Bias[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=35">edit</a>]</h3>
<p>Machine learning approaches in particular can suffer from different data biases. A machine learning system trained specifically on current customers may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the constitutional and unconscious biases already present in society.<sup><a href="#cite_note-:1-100">[100]</a></sup> Language models learned from data have been shown to contain human-like biases.<sup><a href="#cite_note-101">[101]</a></sup><sup><a href="#cite_note-102">[102]</a></sup> Machine learning systems used for criminal risk assessment have been found to be biased against black people.<sup><a href="#cite_note-103">[103]</a></sup><sup><a href="#cite_note-104">[104]</a></sup> In 2015, Google photos would often tag black people as gorillas,<sup><a href="#cite_note-:2-105">[105]</a></sup> and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.<sup><a href="#cite_note-106">[106]</a></sup> Similar issues with recognizing non-white people have been found in many other systems.<sup><a href="#cite_note-107">[107]</a></sup> In 2016, Microsoft tested a <a href="/wiki/Chatbot">chatbot</a> that learned from Twitter, and it quickly picked up racist and sexist language.<sup><a href="#cite_note-108">[108]</a></sup> Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.<sup><a href="#cite_note-109">[109]</a></sup> Concern for <a href="/wiki/Fairness_(machine_learning)">fairness</a> in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including <a href="/wiki/Fei-Fei_Li">Fei-Fei Li</a>, who reminds engineers that "There's nothing artificial about AI...It's inspired by people, it's created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility."<sup><a href="#cite_note-110">[110]</a></sup>
</p>
<h3>Explainability[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=36">edit</a>]</h3>
<p>Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI. It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision. By refining the mental models of users of AI-powered systems and dismantling their misconceptions, XAI promises to help users perform more effectively. XAI may be an implementation of the social right to explanation.
</p>
<h3>Overfitting[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=37">edit</a>]</h3>
<div><p><a href="/wiki/File:Overfitted_Data.png"></a></p><div><p>The blue line could be an example of overfitting a linear function due to random noise.</p></div></div>
<p>Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data but penalizing the theory in accordance with how complex the theory is.<sup><a href="#cite_note-FOOTNOTEDomingos2015Chapter_6,_Chapter_7-10">[10]</a></sup>
</p>
<h3>Other limitations and vulnerabilities[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=38">edit</a>]</h3>
<p>Learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses.<sup><a href="#cite_note-FOOTNOTEDomingos2015286-111">[111]</a></sup> A real-world example is that, unlike humans, current image classifiers often don't primarily make judgments from the spatial relationship between components of the picture, and they learn relationships between pixels that humans are oblivious to, but that still correlate with images of certain types of real objects. Modifying these patterns on a legitimate image can result in "adversarial" images that the system misclassifies.<sup><a href="#cite_note-112">[112]</a></sup><sup><a href="#cite_note-113">[113]</a></sup>
</p><p>Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. Some systems are so brittle that changing a single adversarial pixel predictably induces misclassification.<sup>[<i><a href="/wiki/Wikipedia:Citation_needed">citation needed</a></i>]</sup> Machine learning models are often vulnerable to manipulation and/or evasion via <a href="/wiki/Adversarial_machine_learning">adversarial machine learning</a>.<sup><a href="#cite_note-114">[114]</a></sup>
</p><p>Researchers have demonstrated how <a href="/wiki/Backdoor_(computing)">backdoors</a> can be placed undetectably into classifying (e.g., for categories "spam" and well-visible "not spam" of posts) machine learning models which are often developed and/or trained by third parties. Parties can change the classification of any input, including in cases for which a type of <a href="/wiki/Algorithmic_transparency">data/software transparency</a> is provided, possibly including <a href="/wiki/White-box_testing">white-box access</a>.<sup><a href="#cite_note-115">[115]</a></sup><sup><a href="#cite_note-116">[116]</a></sup><sup><a href="#cite_note-117">[117]</a></sup>
</p>
<h2>Model assessments[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=39">edit</a>]</h2>
<p>Classification of machine learning models can be validated by accuracy estimation techniques like the <a href="/wiki/Test_set">holdout</a> method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-<a href="/wiki/Cross-validation_(statistics)">cross-validation</a> method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, <a href="/wiki/Bootstrapping">bootstrap</a>, which samples n instances with replacement from the dataset, can be used to assess model accuracy.<sup><a href="#cite_note-118">[118]</a></sup>
</p><p>In addition to overall accuracy, investigators frequently report <a href="/wiki/Sensitivity_and_specificity">sensitivity and specificity</a> meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the <a href="/wiki/False_positive_rate">false positive rate</a> (FPR) as well as the <a href="/wiki/False_negative_rate">false negative rate</a> (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The <a href="/wiki/Total_operating_characteristic">total operating characteristic</a> (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used <a href="/wiki/Receiver_operating_characteristic">receiver operating characteristic</a> (ROC) and ROC's associated area under the curve (AUC).<sup><a href="#cite_note-119">[119]</a></sup>
</p>
<h2>Ethics[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=40">edit</a>]</h2>
<p>Machine learning poses a host of <a href="/wiki/Machine_ethics">ethical questions</a>. Systems that are trained on datasets collected with biases may exhibit these biases upon use (<a href="/wiki/Algorithmic_bias">algorithmic bias</a>), thus digitizing cultural prejudices.<sup><a href="#cite_note-120">[120]</a></sup> For example, in 1988, the UK's Commission for Racial Equality found that St. George's Medical School had been using a computer program trained from data of previous admissions staff and this program had denied nearly 60 candidates who were found to be either women or had non-European sounding names.<sup><a href="#cite_note-:1-100">[100]</a></sup> Using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants by similarity to previous successful applicants.<sup><a href="#cite_note-Edionwe_Outline-121">[121]</a></sup><sup><a href="#cite_note-Jeffries_Outline-122">[122]</a></sup> Responsible <a href="/wiki/Data_collection">collection of data</a> and documentation of algorithmic rules used by a system thus is a critical part of machine learning.
</p><p>AI can be well-equipped to make decisions in technical fields, which rely heavily on data and historical information. These decisions rely on the objectivity and logical reasoning.<sup><a href="#cite_note-123">[123]</a></sup> Because human languages contain biases, machines trained on language <i><a href="/wiki/Text_corpus">corpora</a></i> will necessarily also learn these biases.<sup><a href="#cite_note-124">[124]</a></sup><sup><a href="#cite_note-125">[125]</a></sup>
</p><p>Other forms of ethical challenges, not related to personal biases, are seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines.<sup><a href="#cite_note-126">[126]</a></sup> This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increase profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is potential for machine learning in health care to provide professionals an additional tool to diagnose, medicate, and plan recovery paths for patients, but this requires these biases to be mitigated.<sup><a href="#cite_note-127">[127]</a></sup>
</p>
<h2>Hardware[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=41">edit</a>]</h2>
<p>Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training <a href="/wiki/Deep_neural_network">deep neural networks</a> (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.<sup><a href="#cite_note-128">[128]</a></sup> By 2019, graphic processing units (<a href="/wiki/GPU">GPUs</a>), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.<sup><a href="#cite_note-129">[129]</a></sup> <a href="/wiki/OpenAI">OpenAI</a> estimated the hardware computing used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.<sup><a href="#cite_note-130">[130]</a></sup><sup><a href="#cite_note-131">[131]</a></sup>
</p>
<h3>Neuromorphic/Physical Neural Networks[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=42">edit</a>]</h3>
<p>A <a href="/wiki/Physical_neural_network">physical neural network</a> or <a href="/wiki/Neuromorphic_engineering">Neuromorphic computer</a> is a type of <a href="/wiki/Artificial_neural_network">artificial neural network</a> in which an electrically adjustable material is used to emulate the function of a <a href="/wiki/Chemical_synapse">neural synapse</a>. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate <a href="/wiki/Neurons">neurons</a> as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a <a href="/wiki/Memristor">memristor</a> or other electrically adjustable resistance material is used to emulate a neural synapse.<sup><a href="#cite_note-132">[132]</a></sup><sup><a href="#cite_note-133">[133]</a></sup>
</p>
<h3>Embedded Machine Learning[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=43">edit</a>]</h3>
<p><b>Embedded Machine Learning</b> is a sub-field of machine learning, where the machine learning model is run on <a href="/wiki/Embedded_systems">embedded systems</a> with limited computing resources such as <a href="/wiki/Wearable_computer">wearable computers</a>, <a href="/wiki/Edge_device">edge devices</a> and <a href="/wiki/Microcontrollers">microcontrollers</a>.<sup><a href="#cite_note-134">[134]</a></sup><sup><a href="#cite_note-135">[135]</a></sup><sup><a href="#cite_note-136">[136]</a></sup> Running machine learning model in embedded devices removes the need for transferring and storing data on cloud servers for further processing, henceforth, reducing data breaches and privacy leaks happening because of transferring data, and also minimizes theft of intellectual properties, personal data and business secrets. Embedded Machine Learning could be applied through several techniques including <a href="/wiki/Hardware_acceleration">hardware acceleration</a>,<sup><a href="#cite_note-137">[137]</a></sup><sup><a href="#cite_note-138">[138]</a></sup> using <a href="/wiki/Approximate_computing">approximate computing</a>,<sup><a href="#cite_note-139">[139]</a></sup> optimization of machine learning models and many more.<sup><a href="#cite_note-140">[140]</a></sup><sup><a href="#cite_note-141">[141]</a></sup>
</p>
<h2>Software[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=44">edit</a>]</h2>
<p><a href="/wiki/Software_suite">Software suites</a> containing a variety of machine learning algorithms include the following:
</p>
<h3>Free and open-source software[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=45">edit</a>]</h3>
<div>
<ul><li><a href="/wiki/Caffe_(software)">Caffe</a></li>
<li><a href="/wiki/Deeplearning4j">Deeplearning4j</a></li>
<li><a href="/wiki/DeepSpeed">DeepSpeed</a></li>
<li><a href="/wiki/ELKI">ELKI</a></li>
<li><a href="/wiki/Google_JAX">Google JAX</a></li>
<li><a href="/wiki/Infer.NET">Infer.NET</a></li>
<li><a href="/wiki/Keras">Keras</a></li>
<li><a href="/wiki/Kubeflow">Kubeflow</a></li>
<li><a href="/wiki/LightGBM">LightGBM</a></li>
<li><a href="/wiki/Apache_Mahout">Mahout</a></li>
<li><a href="/wiki/Mallet_(software_project)">Mallet</a></li>
<li><a href="/wiki/Microsoft_Cognitive_Toolkit">Microsoft Cognitive Toolkit</a></li>
<li><a href="/wiki/ML.NET">ML.NET</a></li>
<li><a href="/wiki/Mlpack">mlpack</a></li>
<li>MLFlow</li>
<li><a href="/wiki/MXNet">MXNet</a></li>
<li><a href="/wiki/Neural_Lab">Neural Lab</a></li>
<li><a href="/wiki/OpenNN">OpenNN</a></li>
<li><a href="/wiki/Orange_(software)">Orange</a></li>
<li><a href="/wiki/Pandas_(software)">pandas (software)</a></li>
<li><a href="/wiki/ROOT">ROOT</a> (TMVA with ROOT)</li>
<li><a href="/wiki/Scikit-learn">scikit-learn</a></li>
<li><a href="/wiki/Shogun_(toolbox)">Shogun</a></li>
<li><a href="/wiki/Apache_Spark#MLlib_Machine_Learning_Library">Spark MLlib</a></li>
<li><a href="/wiki/Apache_SystemML">SystemML</a></li>
<li><a href="/wiki/TensorFlow">TensorFlow</a></li>
<li><a href="/wiki/Torch_(machine_learning)">Torch</a> / <a href="/wiki/PyTorch">PyTorch</a></li>
<li><a href="/wiki/Weka_(machine_learning)">Weka</a> / <a href="/wiki/MOA_(Massive_Online_Analysis)">MOA</a></li>
<li><a href="/wiki/XGBoost">XGBoost</a></li>
<li><a href="/wiki/Yooreeka">Yooreeka</a></li></ul>
</div>
<h3>Proprietary software with free and open-source editions[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=46">edit</a>]</h3>
<ul><li><a href="/wiki/KNIME">KNIME</a></li>
<li><a href="/wiki/RapidMiner">RapidMiner</a></li></ul>
<h3>Proprietary software[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=47">edit</a>]</h3>
<div>
<ul><li><a href="/wiki/Amazon_Machine_Learning">Amazon Machine Learning</a></li>
<li><a href="/wiki/Angoss">Angoss</a> KnowledgeSTUDIO</li>
<li><a href="/wiki/Azure_Machine_Learning">Azure Machine Learning</a></li>
<li>Ayasdi</li>
<li><a href="/wiki/IBM_Watson_Studio">IBM Watson Studio</a></li>
<li><a href="/wiki/Google_Cloud_Platform#Cloud_AI">Google Cloud Vertex AI</a></li>
<li><a href="/wiki/Google_APIs">Google Prediction API</a></li>
<li><a href="/wiki/SPSS_Modeler">IBM SPSS Modeler</a></li>
<li><a href="/wiki/KXEN_Inc.">KXEN Modeler</a></li>
<li><a href="/wiki/LIONsolver">LIONsolver</a></li>
<li><a href="/wiki/Mathematica">Mathematica</a></li>
<li><a href="/wiki/MATLAB">MATLAB</a></li>
<li><a href="/wiki/Neural_Designer">Neural Designer</a></li>
<li><a href="/wiki/NeuroSolutions">NeuroSolutions</a></li>
<li><a href="/wiki/Oracle_Data_Mining">Oracle Data Mining</a></li>
<li><a href="/wiki/Oracle_Cloud#Platform_as_a_Service_(PaaS)">Oracle AI Platform Cloud Service</a></li>
<li><a href="/wiki/PolyAnalyst">PolyAnalyst</a></li>
<li><a href="/wiki/RCASE">RCASE</a></li>
<li><a href="/wiki/SAS_(software)#Components">SAS Enterprise Miner</a></li>
<li><a href="/wiki/SequenceL">SequenceL</a></li>
<li><a href="/wiki/Splunk">Splunk</a></li>
<li><a href="/wiki/STATISTICA">STATISTICA</a> Data Miner</li></ul>
</div>
<h2>Journals[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=48">edit</a>]</h2>
<ul><li><a href="/wiki/Journal_of_Machine_Learning_Research">Journal of Machine Learning Research</a></li>
<li><a href="/wiki/Machine_Learning_(journal)">Machine Learning</a></li>
<li><a href="/wiki/Nature_Machine_Intelligence">Nature Machine Intelligence</a></li>
<li><a href="/wiki/Neural_Computation_(journal)">Neural Computation</a></li>
<li>IEEE Transactions on Pattern Analysis and Machine Intelligence</li></ul>
<h2>Conferences[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=49">edit</a>]</h2>
<ul><li><a href="/wiki/AAAI_Conference_on_Artificial_Intelligence">AAAI Conference on Artificial Intelligence</a></li>
<li><a href="/wiki/Association_for_Computational_Linguistics">Association for Computational Linguistics (<b>ACL</b>)</a></li>
<li><a href="/wiki/European_Conference_on_Machine_Learning_and_Principles_and_Practice_of_Knowledge_Discovery_in_Databases">European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (<b>ECML PKDD</b>)</a></li>
<li><a href="/wiki/International_Conference_on_Computational_Intelligence_Methods_for_Bioinformatics_and_Biostatistics">International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (<b>CIBB</b>)</a></li>
<li><a href="/wiki/International_Conference_on_Machine_Learning">International Conference on Machine Learning (<b>ICML</b>)</a></li>
<li><a href="/wiki/International_Conference_on_Learning_Representations">International Conference on Learning Representations (<b>ICLR</b>)</a></li>
<li><a href="/wiki/International_Conference_on_Intelligent_Robots_and_Systems">International Conference on Intelligent Robots and Systems (<b>IROS</b>)</a></li>
<li><a href="/wiki/Conference_on_Knowledge_Discovery_and_Data_Mining">Conference on Knowledge Discovery and Data Mining (<b>KDD</b>)</a></li>
<li><a href="/wiki/Conference_on_Neural_Information_Processing_Systems">Conference on Neural Information Processing Systems (<b>NeurIPS</b>)</a></li></ul>
<h2>See also[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=50">edit</a>]</h2>
<ul><li><a href="/wiki/Automated_machine_learning">Automated machine learning</a> – Process of automating the application of machine learning</li>
<li><a href="/wiki/Big_data">Big data</a> – Information assets characterized by high volume, velocity, and variety</li>
<li><a href="/wiki/Differentiable_programming">Differentiable programming</a> – Programming paradigm</li>
<li><a href="/wiki/List_of_important_publications_in_computer_science#Machine_learning">List of important publications in machine learning</a></li>
<li><a href="/wiki/List_of_datasets_for_machine-learning_research">List of datasets for machine-learning research</a></li></ul>
<h2>References[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=51">edit</a>]</h2>
<div>
<ol>
<li><b><a href="#cite_ref-1">^</a></b> <cite><a href="/wiki/Tom_M._Mitchell">Mitchell, Tom</a> (1997). <a href="http://www.cs.cmu.edu/~tom/mlbook.html"><i>Machine Learning</i></a>. New York: McGraw Hill. <a href="/wiki/ISBN_(identifier)">ISBN</a> <a href="/wiki/Special:BookSources/0-07-042807-7"><bdi>0-07-042807-7</bdi></a>. <a href="/wiki/OCLC_(identifier)">OCLC</a> <a href="//www.worldcat.org/oclc/36417892">36417892</a>. <a href="https://web.archive.org/web/20200407075538/http://www.cs.cmu.edu/~tom/mlbook.html">Archived</a> from the original on 2020-04-07. Retrieved 2020-04-09.</cite>
</li>
<li><b><a href="#cite_ref-2">^</a></b> The definition "without being explicitly programmed" is often attributed to <a href="/wiki/Arthur_Samuel_(computer_scientist)">Arthur Samuel</a>, who coined the term "machine learning" in 1959, but the phrase is not found verbatim in this publication, and may be a <a href="/wiki/Paraphrase">paraphrase</a> that appeared later. Confer "Paraphrasing Arthur Samuel (1959), the question is: How can computers learn to solve problems without being explicitly programmed?" in <cite>Koza, John R.; Bennett, Forrest H.; Andre, David; Keane, Martin A. (1996). "Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming". <i>Artificial Intelligence in Design '96</i>. Artificial Intelligence in Design '96. Springer, Dordrecht. pp. 151–170. <a href="/wiki/Doi_(identifier)">doi</a>:<a href="https://doi.org/10.1007%2F978-94-009-0279-4_9">10.1007/978-94-009-0279-4_9</a>. <a href="/wiki/ISBN_(identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-94-010-6610-5"><bdi>978-94-010-6610-5</bdi></a>.</cite>
</li>
<li><b><a href="#cite_ref-tvt_3-0">^</a></b> Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F., "<a href="https://ieeexplore.ieee.org/document/9244647">Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning</a> <a href="https://web.archive.org/web/20201116193020/https://ieeexplore.ieee.org/document/9244647">Archived</a> 2020-11-16 at the <a href="/wiki/Wayback_Machine">Wayback Machine</a>" IEEE Transactions on Vehicular Technology, 2020.
</li>
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<li><b><a href="#cite_ref-130">^</a></b> <cite>Ray, Tiernan (2019). <a href="https://www.zdnet.com/article/ai-is-changing-the-entire-nature-of-compute/">"AI is changing the entire nature of compute"</a>. <i>ZDNet</i>. <a href="https://web.archive.org/web/20200525144635/https://www.zdnet.com/article/ai-is-changing-the-entire-nature-of-compute/">Archived</a> from the original on 25 May 2020. Retrieved 11 June 2020.</cite>
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<li><b><a href="#cite_ref-135">^</a></b> <cite><a href="https://analyticsindiamag.com/a-beginners-guide-to-machine-learning-for-embedded-systems/">"A Beginner's Guide To Machine learning For Embedded Systems"</a>. <i>Analytics India Magazine</i>. 2021-06-02. <a href="https://web.archive.org/web/20220118182754/https://analyticsindiamag.com/a-beginners-guide-to-machine-learning-for-embedded-systems/">Archived</a> from the original on 2022-01-18. Retrieved 2022-01-17.</cite>
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<li><b><a href="#cite_ref-136">^</a></b> <cite>Synced (2022-01-12). <a href="https://syncedreview.com/2022/01/12/deepmind-podracer-tpu-based-rl-frameworks-deliver-exceptional-performance-at-low-cost-183/">"Google, Purdue &amp; Harvard U's Open-Source Framework for TinyML Achieves up to 75x Speedups on FPGAs | Synced"</a>. <i>syncedreview.com</i>. <a href="https://web.archive.org/web/20220118182404/https://syncedreview.com/2022/01/12/deepmind-podracer-tpu-based-rl-frameworks-deliver-exceptional-performance-at-low-cost-183/">Archived</a> from the original on 2022-01-18. Retrieved 2022-01-17.</cite>
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<li><b><a href="#cite_ref-137">^</a></b> <cite>Giri, Davide; Chiu, Kuan-Lin; Di Guglielmo, Giuseppe; Mantovani, Paolo; Carloni, Luca P. (2020-06-15). <a href="https://ieeexplore.ieee.org/abstract/document/9116317?casa_token=5I_Tmgrrbu4AAAAA:v7pDHPEWlRuo2Vk3pU06194PO0-W21UOdyZqADrZxrRdPBZDMLwQrjJSAHUhHtzJmLu_VdgW">"ESP4ML: Platform-Based Design of Systems-on-Chip for Embedded Machine Learning"</a>. <i>2020 Design, Automation Test in Europe Conference Exhibition (DATE)</i>: 1049–1054. <a href="/wiki/ArXiv_(identifier)">arXiv</a>:<a href="//arxiv.org/abs/2004.03640">2004.03640</a>. <a href="/wiki/Doi_(identifier)">doi</a>:<a href="https://doi.org/10.23919%2FDATE48585.2020.9116317">10.23919/DATE48585.2020.9116317</a>. <a href="/wiki/ISBN_(identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-3-9819263-4-7"><bdi>978-3-9819263-4-7</bdi></a>. <a href="/wiki/S2CID_(identifier)">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:210928161">210928161</a>. <a href="https://web.archive.org/web/20220118182342/https://ieeexplore.ieee.org/abstract/document/9116317?casa_token=5I_Tmgrrbu4AAAAA:v7pDHPEWlRuo2Vk3pU06194PO0-W21UOdyZqADrZxrRdPBZDMLwQrjJSAHUhHtzJmLu_VdgW">Archived</a> from the original on 2022-01-18. Retrieved 2022-01-17.</cite>
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<li><b><a href="#cite_ref-138">^</a></b> <cite>Louis, Marcia Sahaya; Azad, Zahra; Delshadtehrani, Leila; Gupta, Suyog; Warden, Pete; Reddi, Vijay Janapa; Joshi, Ajay (2019). <a href="https://edge.seas.harvard.edu/publications/towards-deep-learning-using-tensorflow-lite-risc-v">"Towards Deep Learning using TensorFlow Lite on RISC-V"</a>. <i><a href="/wiki/Harvard_University">Harvard University</a></i>. <a href="https://web.archive.org/web/20220117031909/https://edge.seas.harvard.edu/publications/towards-deep-learning-using-tensorflow-lite-risc-v">Archived</a> from the original on 2022-01-17. Retrieved 2022-01-17.</cite>
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<li><b><a href="#cite_ref-139">^</a></b> <cite>Ibrahim, Ali; Osta, Mario; Alameh, Mohamad; Saleh, Moustafa; Chible, Hussein; Valle, Maurizio (2019-01-21). <a href="https://ieeexplore.ieee.org/abstract/document/8617877?casa_token=arUW5Oy-tzwAAAAA:I9x6edlfskM6kGNFUN9zAFrjEBv_8kYTz7ERTxtXu9jAqdrYCcDbbwjBdgwXvb6QAH_-0VJJ">"Approximate Computing Methods for Embedded Machine Learning"</a>. <i>2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS)</i>: 845–848. <a href="/wiki/Doi_(identifier)">doi</a>:<a href="https://doi.org/10.1109%2FICECS.2018.8617877">10.1109/ICECS.2018.8617877</a>. <a href="/wiki/ISBN_(identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-1-5386-9562-3"><bdi>978-1-5386-9562-3</bdi></a>. <a href="/wiki/S2CID_(identifier)">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:58670712">58670712</a>. <a href="https://web.archive.org/web/20220117031855/https://ieeexplore.ieee.org/abstract/document/8617877?casa_token=arUW5Oy-tzwAAAAA:I9x6edlfskM6kGNFUN9zAFrjEBv_8kYTz7ERTxtXu9jAqdrYCcDbbwjBdgwXvb6QAH_-0VJJ">Archived</a> from the original on 2022-01-17. Retrieved 2022-01-17.</cite>
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<li><b><a href="#cite_ref-140">^</a></b> <cite><a href="https://dblp.org/rec/journals/corr/abs-1903-01855.html">"dblp: TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning"</a>. <i>dblp.org</i>. <a href="https://web.archive.org/web/20220118182335/https://dblp.org/rec/journals/corr/abs-1903-01855.html">Archived</a> from the original on 2022-01-18. Retrieved 2022-01-17.</cite>
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<li><b><a href="#cite_ref-141">^</a></b> <cite>Branco, Sérgio; Ferreira, André G.; Cabral, Jorge (2019-11-05). <a href="https://doi.org/10.3390%2Felectronics8111289">"Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey"</a>. <i>Electronics</i>. <b>8</b> (11): 1289. <a href="/wiki/Doi_(identifier)">doi</a>:<a href="https://doi.org/10.3390%2Felectronics8111289">10.3390/electronics8111289</a>. <a href="/wiki/ISSN_(identifier)">ISSN</a> <a href="//www.worldcat.org/issn/2079-9292">2079-9292</a>.</cite>
</li>
</ol></div>
<h2>Sources[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=52">edit</a>]</h2>
<ul><li><cite><a href="/wiki/Pedro_Domingos">Domingos, Pedro</a> (September 22, 2015). <i>The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World</i>. <a href="/wiki/Basic_Books">Basic Books</a>. <a href="/wiki/ISBN_(identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0465065707"><bdi>978-0465065707</bdi></a>.</cite></li>
<li><cite><a href="/wiki/Nils_Nilsson_(researcher)">Nilsson, Nils</a> (1998). <a href="https://archive.org/details/artificialintell0000nils"><i>Artificial Intelligence: A New Synthesis</i></a>. Morgan Kaufmann. <a href="/wiki/ISBN_(identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-1-55860-467-4"><bdi>978-1-55860-467-4</bdi></a>. <a href="https://web.archive.org/web/20200726131654/https://archive.org/details/artificialintell0000nils">Archived</a> from the original on 26 July 2020. Retrieved 18 November 2019.</cite></li>
<li><cite><a href="/wiki/Stuart_J._Russell">Russell, Stuart J.</a>; <a href="/wiki/Peter_Norvig">Norvig, Peter</a> (2003), <a href="http://aima.cs.berkeley.edu/"><i>Artificial Intelligence: A Modern Approach</i></a> (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, <a href="/wiki/ISBN_(identifier)">ISBN</a> <a href="/wiki/Special:BookSources/0-13-790395-2"><bdi>0-13-790395-2</bdi></a></cite>.</li>
<li><cite>Poole, David; <a href="/wiki/Alan_Mackworth">Mackworth, Alan</a>; Goebel, Randy (1998). <a href="https://archive.org/details/computationalint00pool"><i>Computational Intelligence: A Logical Approach</i></a>. New York: Oxford University Press. <a href="/wiki/ISBN_(identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-19-510270-3"><bdi>978-0-19-510270-3</bdi></a>. <a href="https://web.archive.org/web/20200726131436/https://archive.org/details/computationalint00pool">Archived</a> from the original on 26 July 2020. Retrieved 22 August 2020.</cite></li></ul>
<h2>Further reading[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=53">edit</a>]</h2>
<div>
<ul><li>Nils J. Nilsson, <i><a href="https://ai.stanford.edu/people/nilsson/mlbook.html">Introduction to Machine Learning</a> <a href="https://web.archive.org/web/20190816182600/http://ai.stanford.edu/people/nilsson/mlbook.html">Archived</a> 2019-08-16 at the <a href="/wiki/Wayback_Machine">Wayback Machine</a></i>.</li>
<li><a href="/wiki/Trevor_Hastie">Trevor Hastie</a>, <a href="/wiki/Robert_Tibshirani">Robert Tibshirani</a> and <a href="/wiki/Jerome_H._Friedman">Jerome H. Friedman</a> (2001). <i><a href="https://web.stanford.edu/~hastie/ElemStatLearn/">The Elements of Statistical Learning</a> <a href="https://web.archive.org/web/20131027220938/http://www-stat.stanford.edu/%7Etibs/ElemStatLearn//">Archived</a> 2013-10-27 at the <a href="/wiki/Wayback_Machine">Wayback Machine</a></i>, Springer. <a href="/wiki/ISBN_(identifier)">ISBN</a> <a href="/wiki/Special:BookSources/0-387-95284-5">0-387-95284-5</a>.</li>
<li><a href="/wiki/Pedro_Domingos">Pedro Domingos</a> (September 2015), <i><a href="/wiki/The_Master_Algorithm">The Master Algorithm</a></i>, Basic Books, <a href="/wiki/ISBN_(identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-465-06570-7">978-0-465-06570-7</a></li>
<li>Ian H. Witten and Eibe Frank (2011). <i>Data Mining: Practical machine learning tools and techniques</i> Morgan Kaufmann, 664pp., <a href="/wiki/ISBN_(identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-12-374856-0">978-0-12-374856-0</a>.</li>
<li>Ethem Alpaydin (2004). <i>Introduction to Machine Learning</i>, MIT Press, <a href="/wiki/ISBN_(identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-262-01243-0">978-0-262-01243-0</a>.</li>
<li><a href="/wiki/David_J._C._MacKay">David J. C. MacKay</a>. <i><a href="http://www.inference.phy.cam.ac.uk/mackay/itila/book.html">Information Theory, Inference, and Learning Algorithms</a> <a href="https://web.archive.org/web/20160217105359/http://www.inference.phy.cam.ac.uk/mackay/itila/book.html">Archived</a> 2016-02-17 at the <a href="/wiki/Wayback_Machine">Wayback Machine</a></i> Cambridge: Cambridge University Press, 2003. <a href="/wiki/ISBN_(identifier)">ISBN</a> <a href="/wiki/Special:BookSources/0-521-64298-1">0-521-64298-1</a></li>
<li><a href="/wiki/Richard_O._Duda">Richard O. Duda</a>, <a href="/wiki/Peter_E._Hart">Peter E. Hart</a>, David G. Stork (2001) <i>Pattern classification</i> (2nd edition), Wiley, New York, <a href="/wiki/ISBN_(identifier)">ISBN</a> <a href="/wiki/Special:BookSources/0-471-05669-3">0-471-05669-3</a>.</li>
<li><a href="/wiki/Christopher_Bishop">Christopher Bishop</a> (1995). <i>Neural Networks for Pattern Recognition</i>, Oxford University Press. <a href="/wiki/ISBN_(identifier)">ISBN</a> <a href="/wiki/Special:BookSources/0-19-853864-2">0-19-853864-2</a>.</li>
<li>Stuart Russell &amp; Peter Norvig, (2009). <i><a href="http://aima.cs.berkeley.edu/">Artificial Intelligence – A Modern Approach</a> <a href="https://web.archive.org/web/20110228023805/http://aima.cs.berkeley.edu/">Archived</a> 2011-02-28 at the <a href="/wiki/Wayback_Machine">Wayback Machine</a></i>. Pearson, <a href="/wiki/ISBN_(identifier)">ISBN</a> <a href="/wiki/Special:BookSources/9789332543515">9789332543515</a>.</li>
<li><a href="/wiki/Ray_Solomonoff">Ray Solomonoff</a>, <i>An Inductive Inference Machine</i>, IRE Convention Record, Section on Information Theory, Part 2, pp., 56–62, 1957.</li>
<li><a href="/wiki/Ray_Solomonoff">Ray Solomonoff</a>, <i><a href="http://world.std.com/~rjs/indinf56.pdf">An Inductive Inference Machine</a> <a href="https://web.archive.org/web/20110426161749/http://world.std.com/~rjs/indinf56.pdf">Archived</a> 2011-04-26 at the <a href="/wiki/Wayback_Machine">Wayback Machine</a></i> A privately circulated report from the 1956 <a href="/wiki/Dartmouth_workshop">Dartmouth Summer Research Conference on AI</a>.</li>
<li>Kevin P. Murphy (2021). <i><a href="https://probml.github.io/pml-book/book1.html">Probabilistic Machine Learning: An Introduction</a> <a href="https://web.archive.org/web/20210411153246/https://probml.github.io/pml-book/book1.html">Archived</a> 2021-04-11 at the <a href="/wiki/Wayback_Machine">Wayback Machine</a></i>, MIT Press.</li></ul>
</div>
<h2>External links[<a href="/w/index.php?title=Machine_learning&amp;action=edit&amp;section=54">edit</a>]</h2>
<ul><li><a href="/wiki/File:Wikiquote-logo.svg"></a> Quotations related to <a href="https://en.wikiquote.org/wiki/Special:Search/Machine_learning">Machine learning</a> at Wikiquote</li>
<li><a href="https://web.archive.org/web/20171230081341/http://machinelearning.org/">International Machine Learning Society</a></li>
<li><a href="https://mloss.org/">mloss</a> is an academic database of open-source machine learning software.</li></ul>
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 Contents

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 * (Top)
 * 1 Overview
 * 2 History and relationships to other fields
 + 2.1 Artificial intelligence
 + 2.2 Data mining
 + 2.3 Optimization
 + 2.4 Generalization
 + 2.5 Statistics
 + 2.6 Physics
 * 3 Theory
 * 4 Approaches
 + 4.1 Supervised learning
 + 4.2 Unsupervised learning
 + 4.3 Semi-supervised learning
 + 4.4 Reinforcement learning
 + 4.5 Dimensionality reduction
 + 4.6 Other types
 o 4.6.1 Self-learning
 o 4.6.2 Feature learning
 o 4.6.3 Sparse dictionary learning
 o 4.6.4 Anomaly detection
 o 4.6.5 Robot learning
 o 4.6.6 Association rules
 + 4.7 Models
 o 4.7.1 Artificial neural networks
 o 4.7.2 Decision trees
 o 4.7.3 Support-vector machines
 o 4.7.4 Regression analysis
 o 4.7.5 Bayesian networks
 o 4.7.6 Gaussian processes
 o 4.7.7 Genetic algorithms
 + 4.8 Training models
 o 4.8.1 Federated learning
 * 5 Applications
 * 6 Limitations
 + 6.1 Bias
 + 6.2 Explainability
 + 6.3 Overfitting
 + 6.4 Other limitations and vulnerabilities
 * 7 Model assessments
 * 8 Ethics
 * 9 Hardware
 + 9.1 Neuromorphic/Physical Neural Networks
 + 9.2 Embedded Machine Learning
 * 10 Software
 + 10.1 Free and open-source software
 + 10.2 Proprietary software with free and open-source editions
 + 10.3 Proprietary software
 * 11 Journals
 * 12 Conferences
 * 13 See also
 * 14 References
 * 15 Sources
 * 16 Further reading
 * 17 External links
 Toggle the table of contents

 Machine learning

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 From Wikipedia, the free encyclopedia
 For the journal, see Machine Learning (journal).
 "Statistical learning" redirects here. For statistical learning in linguistics, see statistical learning in language acquisition.
 Part of a series on 
 Machine learning 
 and data mining 
  
 Paradigms 
 * Supervised learning 
 * Unsupervised learning 
 * Online learning 
 * Batch learning 
 * Semi-supervised learning 
 * Self-supervised learning 
 * Reinforcement learning 
 Problems 
 * Classification 
 * Regression 
 * Clustering 
 * dimension reduction 
 * density estimation 
 * Anomaly detection 
 * Data Cleaning 
 * AutoML 
 * Association rules 
 * Structured prediction 
 * Feature engineering 
 * Feature learning 
 * Learning to rank 
 * Grammar induction 
 Supervised learning 
 ( classification • regression ) 
 * Decision trees 
 * Ensembles 
 + Bagging 
 + Boosting 
 + Random forest 
 * k-NN 
 * Linear regression 
 * Naive Bayes 
 * Artificial neural networks 
 * Logistic regression 
 * Perceptron 
 * Relevance vector machine (RVM) 
 * Support vector machine (SVM) 
 Clustering 
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 * CURE 
 * Hierarchical 
 * k-means 
 * Fuzzy 
 * Expectation–maximization (EM) 
  
 * DBSCAN 
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 Dimensionality reduction 
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 * PGD 
 * t-SNE 
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 Structured prediction 
 * Graphical models 
 + Bayes net 
 + Conditional random field 
 + Hidden Markov 
 Anomaly detection 
 * RANSAC 
 * k-NN 
 * Local outlier factor 
 * Isolation forest 
 Artificial neural network 
 * Autoencoder 
 * Cognitive computing 
 * Deep learning 
 * DeepDream 
 * Multilayer perceptron 
 * RNN 
 + LSTM 
 + GRU 
 + ESN 
 + reservoir computing 
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 * Transformer 
 + Vision 
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 Reinforcement learning 
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 + Self-play 
 Learning with humans 
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 * Human-in-the-loop 
 Model diagnostics 
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 Theory 
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 * Bias–variance tradeoff 
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 Machine-learning venues 
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 * Glossary of artificial intelligence 
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 Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks.[1] It is seen as a part of artificial intelligence.

 Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.[2] Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.[3][4]

 A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers, but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning.[6][7]

 Some implementations of machine learning use data and neural networks in a way that mimics the working of a biological brain.[8][9]

 In its application across business problems, machine learning is also referred to as predictive analytics.

 Overview [ edit ]

 Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". They can be nuanced, such as "X% of families have geographically separate species with color variants, so there is a Y% chance that undiscovered black swans exist".[10]

 Machine learning programs can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than having human programmers specify every needed step.[11]

 The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available. In cases where vast numbers of potential answers exist, one approach is to label some of the correct answers as valid. This can then be used as training data for the computer to improve the algorithm(s) it uses to determine correct answers. For example, to train a system for the task of digital character recognition, the MNIST dataset of handwritten digits has often been used.[11]

 History and relationships to other fields [ edit ]

 See also: Timeline of machine learning

 The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence.[12][13] The synonym self-teaching computers was also used in this time period.[14][15]

 By the early 1960s an experimental "learning machine" with punched tape memory, called CyberTron, had been developed by Raytheon Company to analyze sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognize patterns and equipped with a "goof" button to cause it to re-evaluate incorrect decisions.[16] A representative book on research into machine learning during the 1960s was Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification.[17] Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973.[18] In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.[19]

 Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."[20] This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".[21]

 Modern-day machine learning has two objectives, one is to classify data based on models which have been developed, the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. A machine learning algorithm for stock trading may inform the trader of future potential predictions.[22]

 Artificial intelligence [ edit ]

 Machine learning as subfield of AI[23]
 Part of machine learning as subfield of AI or part of AI as subfield of machine learning[24]

 As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what was then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics.[25] Probabilistic reasoning was also employed, especially in automated medical diagnosis.[26] : 488

 However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.[26] : 488 By 1980, expert systems had come to dominate AI, and statistics was out of favor.[27] Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.[26] : 708–710, 755 Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including Hopfield, Rumelhart, and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.[26] : 25

 Machine learning (ML), reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory.[27]

 The difference between ML and AI is frequently misunderstood. ML learns and predicts based on passive observations, whereas AI implies an agent interacting with the environment to learn and take actions that maximize its chance of successfully achieving its goals.[28]

 As of 2020, many sources continue to assert that ML remains a subfield of AI.[29][30][27] Others have the view that not all ML is part of AI, but only an 'intelligent subset' of ML should be considered AI.[5][31][32]

 Data mining [ edit ]

 Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.

 Optimization [ edit ]

 Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples).[33]

 Generalization [ edit ]

 The difference between optimization and machine learning arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples. Characterizing the generalization of various learning algorithms is an active topic of current research, especially for deep learning algorithms.

 Statistics [ edit ]

 Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns.[34] According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.[35] He also suggested the term data science as a placeholder to call the overall field.[35]

 Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model,[29] wherein "algorithmic model" means more or less the machine learning algorithms like Random Forest.

 Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.[30]

 Physics [ edit ]

 Analytical and computational techniques derived from statistical physics of disordered systems, can be extended to large-scale problems, including machine learning, e.g., to analyze the weight space of deep neural networks.[36] Statistical physics is thus finding applications in the area of medical diagnostics.[37]

 Theory [ edit ]

 Main articles: Computational learning theory and Statistical learning theory

 A core objective of a learner is to generalize from its experience.[5][31] Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.

 The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory via the Probably Approximately Correct Learning (PAC) model. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error.

 For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer.[32]

 In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results: Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.

 Approaches [ edit ]

 Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on the nature of the "signal" or "feedback" available to the learning system:

 * Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
 * Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
 * Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize.[5]

 Supervised learning [ edit ]

 Main article: Supervised learning
 A support-vector machine is a supervised learning model that divides the data into regions separated by a linear boundary. Here, the linear boundary divides the black circles from the white.

 Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.[38] The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs.[39] An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.[20]

 Types of supervised-learning algorithms include active learning, classification and regression.[28] Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email.

 Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification.

 Unsupervised learning [ edit ]

 Main article: Unsupervised learning
 See also: Cluster analysis

 Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics, such as finding the probability density function.[40] Though unsupervised learning encompasses other domains involving summarizing and explaining data features.

 Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity.

 Semi-supervised learning [ edit ]

 Main article: Semi-supervised learning

 Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy.

 In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets.[41]

 Reinforcement learning [ edit ]

 Main article: Reinforcement learning

 Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In machine learning, the environment is typically represented as a Markov decision process (MDP). Many reinforcements learning algorithms use dynamic programming techniques.[42] Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent.

 Dimensionality reduction [ edit ]

 Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables.[43] In other words, it is a process of reducing the dimension of the feature set, also called the "number of features". Most of the dimensionality reduction techniques can be considered as either feature elimination or extraction. One of the popular methods of dimensionality reduction is principal component analysis (PCA). PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D). This results in a smaller dimension of data (2D instead of 3D), while keeping all original variables in the model without changing the data.[44] The manifold hypothesis proposes that high-dimensional data sets lie along low-dimensional manifolds, and many dimensionality reduction techniques make this assumption, leading to the area of manifold learning and manifold regularization.

 Other types [ edit ]

 Other approaches have been developed which don't fit neatly into this three-fold categorization, and sometimes more than one is used by the same machine learning system. For example, topic modeling, meta-learning.[45]

 As of 2022, deep learning is the dominant approach for much ongoing work in the field of machine learning.[11]

 Self-learning [ edit ]

 Self-learning, as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning, named crossbar adaptive array (CAA).[46] It is learning with no external rewards and no external teacher advice. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion.[47] The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine:

 1. in situation s perform action a
 2. receive consequence situation s'
 3. compute emotion of being in consequence situation v(s')
 4. update crossbar memory w'(a,s) = w(a,s) + v(s')

 It is a system with only one input, situation, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is the behavioral environment where it behaves, and the other is the genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal-seeking behavior, in an environment that contains both desirable and undesirable situations.[48]

 Feature learning [ edit ]

 Main article: Feature learning

 Several learning algorithms aim at discovering better representations of the inputs provided during training.[49] Classic examples include principal component analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task.

 Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization[50] and various forms of clustering.[51][52][53]

 Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors.[54] Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.[55]

 Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.

 Sparse dictionary learning [ edit ]

 Main article: Sparse dictionary learning

 Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately.[56] A popular heuristic method for sparse dictionary learning is the K-SVD algorithm. Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine the class to which a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot.[57]

 Anomaly detection [ edit ]

 Main article: Anomaly detection

 In data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.[58] Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are referred to as outliers, novelties, noise, deviations and exceptions.[59]

 In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts of inactivity. This pattern does not adhere to the common statistical definition of an outlier as a rare object. Many outlier detection methods (in particular, unsupervised algorithms) will fail on such data unless aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns.[60]

 Three broad categories of anomaly detection techniques exist.[61] Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit the least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set and then test the likelihood of a test instance to be generated by the model.

 Robot learning [ edit ]

 Robot learning is inspired by a multitude of machine learning methods, starting from supervised learning, reinforcement learning,[62][63] and finally meta-learning (e.g. MAML).

 Association rules [ edit ]

 Main article: Association rule learning
 See also: Inductive logic programming

 Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[64]

 Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[65] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems.

 Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[66] For example, the rule found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions.

 Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[67]

 Inductive logic programming (ILP) is an approach to rule learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs.

 Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[68][69][70] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[71] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set.

 Models [ edit ]

 Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems.

 Artificial neural networks [ edit ]

 Main article: Artificial neural network
 See also: Deep learning
 An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another.

 Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.

 An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times.

 The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.

 Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[72]

 Decision trees [ edit ]

 Main article: Decision tree learning
 A decision tree showing survival probability of passengers on the Titanic

 Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels, and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision-making.

 Support-vector machines [ edit ]

 Main article: Support-vector machine

 Support-vector machines (SVMs), also known as support-vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category.[73] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.

 Regression analysis [ edit ]

 Main article: Regression analysis
 Illustration of linear regression on a data set

 Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[74]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space.

 Bayesian networks [ edit ]

 Main article: Bayesian network
 A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet.

 A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.

 Gaussian processes [ edit ]

 Main article: Gaussian processes
 An example of Gaussian Process Regression (prediction) compared with other regression models[75]

 A Gaussian process is a stochastic process in which every finite collection of the random variables in the process has a multivariate normal distribution, and it relies on a pre-defined covariance function, or kernel, that models how pairs of points relate to each other depending on their locations.

 Given a set of observed points, or input–output examples, the distribution of the (unobserved) output of a new point as function of its input data, can be directly computed by looking like the observed points and the covariances between those points and the new, unobserved point.

 Gaussian processes are popular surrogate models in Bayesian optimization used to do hyperparameter optimization.

 Genetic algorithms [ edit ]

 Main article: Genetic algorithm

 A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[76][77] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[78]

 Training models [ edit ]

 Typically, machine learning models require a high quantity of reliable data in order for the models to perform accurate predictions. When training a machine learning model, machine learning engineers need to target and collect a large and representative sample of data. Data from the training set can be as varied as a corpus of text, a collection of images, sensor data, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Trained models derived from biased or non-evaluated data can result in skewed or undesired predictions. Bias models may result in detrimental outcomes thereby furthering the negative impacts on society or objectives. Algorithmic bias is a potential result of data not being fully prepared for training. Machine learning ethics is becoming a field of study and notably be integrated within machine learning engineering teams.

 Federated learning [ edit ]

 Main article: Federated learning

 Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[79]

 Applications [ edit ]

 There are many applications for machine learning, including:

 * Agriculture
 * Anatomy
 * Adaptive website
 * Affective computing
 * Astronomy
 * Automated decision-making
 * Banking
 * Behaviorism
 * Bioinformatics
 * Brain–machine interfaces
 * Cheminformatics
 * Citizen Science
 * Climate Science
 * Computer networks
 * Computer vision
 * Credit-card fraud detection
 * Data quality
 * DNA sequence classification
 * Economics
 * Financial market analysis[80]
 * General game playing
 * Handwriting recognition
 * Information retrieval
 * Insurance
 * Internet fraud detection
 * Knowledge graph embedding
 * Linguistics
 * Machine learning control
 * Machine perception
 * Machine translation
 * Marketing
 * Medical diagnosis
 * Natural language processing
 * Natural language understanding
 * Online advertising
 * Optimization
 * Recommender systems
 * Robot locomotion
 * Search engines
 * Sentiment analysis
 * Sequence mining
 * Software engineering
 * Speech recognition
 * Structural health monitoring
 * Syntactic pattern recognition
 * Telecommunication
 * Theorem proving
 * Time-series forecasting
 * User behavior analytics

 In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[81] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[82] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[83] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[84] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[85] In 2019 Springer Nature published the first research book created using machine learning.[86] In 2020, machine learning technology was used to help make diagnoses and aid researchers in developing a cure for COVID-19.[87] Machine learning was recently applied to predict the pro-environmental behavior of travelers.[88] Recently, machine learning technology was also applied to optimize smartphone's performance and thermal behavior based on the user's interaction with the phone.[89][90][91]

 Limitations [ edit ]

 Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[92][93][94] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[95]

 In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[96] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[97][98]

 Machine learning has been used as a strategy to update the evidence related to a systematic review and increased reviewer burden related to the growth of biomedical literature. While it has improved with training sets, it has not yet developed sufficiently to reduce the workload burden without limiting the necessary sensitivity for the findings research themselves.[99]

 Bias [ edit ]

 Main article: Algorithmic bias

 Machine learning approaches in particular can suffer from different data biases. A machine learning system trained specifically on current customers may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the constitutional and unconscious biases already present in society.[100] Language models learned from data have been shown to contain human-like biases.[101][102] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[103][104] In 2015, Google photos would often tag black people as gorillas,[105] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[106] Similar issues with recognizing non-white people have been found in many other systems.[107] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[108] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[109] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There's nothing artificial about AI...It's inspired by people, it's created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility."[110]

 Explainability [ edit ]

 Main article: Explainable artificial intelligence

 Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI. It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision. By refining the mental models of users of AI-powered systems and dismantling their misconceptions, XAI promises to help users perform more effectively. XAI may be an implementation of the social right to explanation.

 Overfitting [ edit ]

 Main article: Overfitting
 The blue line could be an example of overfitting a linear function due to random noise.

 Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data but penalizing the theory in accordance with how complex the theory is.[10]

 Other limitations and vulnerabilities [ edit ]

 Learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses.[111] A real-world example is that, unlike humans, current image classifiers often don't primarily make judgments from the spatial relationship between components of the picture, and they learn relationships between pixels that humans are oblivious to, but that still correlate with images of certain types of real objects. Modifying these patterns on a legitimate image can result in "adversarial" images that the system misclassifies.[112][113]

 Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. Some systems are so brittle that changing a single adversarial pixel predictably induces misclassification.[ citation needed ] Machine learning models are often vulnerable to manipulation and/or evasion via adversarial machine learning.[114]

 Researchers have demonstrated how backdoors can be placed undetectably into classifying (e.g., for categories "spam" and well-visible "not spam" of posts) machine learning models which are often developed and/or trained by third parties. Parties can change the classification of any input, including in cases for which a type of data/software transparency is provided, possibly including white-box access.[115][116][117]

 Model assessments [ edit ]

 Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[118]

 In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[119]

 Ethics [ edit ]

 See also: AI control problem and Toronto Declaration

 Machine learning poses a host of ethical questions. Systems that are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[120] For example, in 1988, the UK's Commission for Racial Equality found that St. George's Medical School had been using a computer program trained from data of previous admissions staff and this program had denied nearly 60 candidates who were found to be either women or had non-European sounding names.[100] Using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants by similarity to previous successful applicants.[121][122] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning.

 AI can be well-equipped to make decisions in technical fields, which rely heavily on data and historical information. These decisions rely on the objectivity and logical reasoning.[123] Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[124][125]

 Other forms of ethical challenges, not related to personal biases, are seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines.[126] This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increase profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is potential for machine learning in health care to provide professionals an additional tool to diagnose, medicate, and plan recovery paths for patients, but this requires these biases to be mitigated.[127]

 Hardware [ edit ]

 Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[128] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[129] OpenAI estimated the hardware computing used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[130][131]

 Neuromorphic/Physical Neural Networks [ edit ]

 A physical neural network or Neuromorphic computer is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse.[132][133]

 Embedded Machine Learning [ edit ]

 Embedded Machine Learning is a sub-field of machine learning, where the machine learning model is run on embedded systems with limited computing resources such as wearable computers, edge devices and microcontrollers.[134][135][136] Running machine learning model in embedded devices removes the need for transferring and storing data on cloud servers for further processing, henceforth, reducing data breaches and privacy leaks happening because of transferring data, and also minimizes theft of intellectual properties, personal data and business secrets. Embedded Machine Learning could be applied through several techniques including hardware acceleration,[137][138] using approximate computing,[139] optimization of machine learning models and many more.[140][141]

 Software [ edit ]

 Software suites containing a variety of machine learning algorithms include the following:

 Free and open-source software [ edit ]

 * Caffe
 * Deeplearning4j
 * DeepSpeed
 * ELKI
 * Google JAX
 * Infer.NET
 * Keras
 * Kubeflow
 * LightGBM
 * Mahout
 * Mallet
 * Microsoft Cognitive Toolkit
 * ML.NET
 * mlpack
 * MLFlow
 * MXNet
 * Neural Lab
 * OpenNN
 * Orange
 * pandas (software)
 * ROOT (TMVA with ROOT)
 * scikit-learn
 * Shogun
 * Spark MLlib
 * SystemML
 * TensorFlow
 * Torch / PyTorch
 * Weka / MOA
 * XGBoost
 * Yooreeka

 Proprietary software with free and open-source editions [ edit ]

 * KNIME
 * RapidMiner

 Proprietary software [ edit ]

 * Amazon Machine Learning
 * Angoss KnowledgeSTUDIO
 * Azure Machine Learning
 * Ayasdi
 * IBM Watson Studio
 * Google Cloud Vertex AI
 * Google Prediction API
 * IBM SPSS Modeler
 * KXEN Modeler
 * LIONsolver
 * Mathematica
 * MATLAB
 * Neural Designer
 * NeuroSolutions
 * Oracle Data Mining
 * Oracle AI Platform Cloud Service
 * PolyAnalyst
 * RCASE
 * SAS Enterprise Miner
 * SequenceL
 * Splunk
 * STATISTICA Data Miner

 Journals [ edit ]

 * Journal of Machine Learning Research
 * Machine Learning
 * Nature Machine Intelligence
 * Neural Computation
 * IEEE Transactions on Pattern Analysis and Machine Intelligence

 Conferences [ edit ]

 * AAAI Conference on Artificial Intelligence
 * Association for Computational Linguistics (ACL)
 * European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
 * International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB)
 * International Conference on Machine Learning (ICML)
 * International Conference on Learning Representations (ICLR)
 * International Conference on Intelligent Robots and Systems (IROS)
 * Conference on Knowledge Discovery and Data Mining (KDD)
 * Conference on Neural Information Processing Systems (NeurIPS)

 See also [ edit ]

 * Automated machine learning – Process of automating the application of machine learning
 * Big data – Information assets characterized by high volume, velocity, and variety
 * Differentiable programming – Programming paradigm
 * List of important publications in machine learning
 * List of datasets for machine-learning research

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 Sources [ edit ]

 * Domingos, Pedro (September 22, 2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. ISBN 978-0465065707.
 * Nilsson, Nils (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann. ISBN 978-1-55860-467-4. Archived from the original on 26 July 2020 . Retrieved 18 November 2019 .
 * Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2.
 * Poole, David; Mackworth, Alan; Goebel, Randy (1998). Computational Intelligence: A Logical Approach. New York: Oxford University Press. ISBN 978-0-19-510270-3. Archived from the original on 26 July 2020 . Retrieved 22 August 2020 .

 Further reading [ edit ]

 * Nils J. Nilsson, Introduction to Machine Learning Archived 2019-08-16 at the Wayback Machine.
 * Trevor Hastie, Robert Tibshirani and Jerome H. Friedman (2001). The Elements of Statistical Learning Archived 2013-10-27 at the Wayback Machine, Springer. ISBN 0-387-95284-5.
 * Pedro Domingos (September 2015), The Master Algorithm, Basic Books, ISBN 978-0-465-06570-7
 * Ian H. Witten and Eibe Frank (2011). Data Mining: Practical machine learning tools and techniques Morgan Kaufmann, 664pp., ISBN 978-0-12-374856-0.
 * Ethem Alpaydin (2004). Introduction to Machine Learning, MIT Press, ISBN 978-0-262-01243-0.
 * David J. C. MacKay. Information Theory, Inference, and Learning Algorithms Archived 2016-02-17 at the Wayback Machine Cambridge: Cambridge University Press, 2003. ISBN 0-521-64298-1
 * Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN 0-471-05669-3.
 * Christopher Bishop (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN 0-19-853864-2.
 * Stuart Russell & Peter Norvig, (2009). Artificial Intelligence – A Modern Approach Archived 2011-02-28 at the Wayback Machine. Pearson, ISBN 9789332543515.
 * Ray Solomonoff, An Inductive Inference Machine, IRE Convention Record, Section on Information Theory, Part 2, pp., 56–62, 1957.
 * Ray Solomonoff, An Inductive Inference Machine Archived 2011-04-26 at the Wayback Machine A privately circulated report from the 1956 Dartmouth Summer Research Conference on AI.
 * Kevin P. Murphy (2021). Probabilistic Machine Learning: An Introduction Archived 2021-04-11 at the Wayback Machine, MIT Press.

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Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. [1]  It is seen as a part of artificial intelligence. 
 Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. [2]  Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. [3]   [4] 
 A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers, but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. [6]   [7]  
 Some implementations of machine learning use data and neural networks in a way that mimics the working of a biological brain. [8]   [9]  
 In its application across business problems, machine learning is also referred to as predictive analytics.
Overview[edit]
Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". They can be nuanced, such as "X% of families have geographically separate species with color variants, so there is a Y% chance that undiscovered black swans exist". [10] 
 Machine learning programs can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than having human programmers specify every needed step. [11] 
 The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available. In cases where vast numbers of potential answers exist, one approach is to label some of the correct answers as valid. This can then be used as training data for the computer to improve the algorithm(s) it uses to determine correct answers. For example, to train a system for the task of digital character recognition, the MNIST dataset of handwritten digits has often been used. [11] 
History and relationships to other fields[edit]
The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence. [12]   [13]  The synonym self-teaching computers was also used in this time period. [14]   [15] 
 By the early 1960s an experimental "learning machine" with punched tape memory, called CyberTron, had been developed by Raytheon Company to analyze sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognize patterns and equipped with a "goof" button to cause it to re-evaluate incorrect decisions. [16]  A representative book on research into machine learning during the 1960s was Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. [17]  Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. [18]  In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal. [19] 
  Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." [20]  This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?". [21] 
 Modern-day machine learning has two objectives, one is to classify data based on models which have been developed, the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. A machine learning algorithm for stock trading may inform the trader of future potential predictions. [22] 
Artificial intelligence[edit]
      Machine learning as subfield of AI [23]    
      Part of machine learning as subfield of AI or part of AI as subfield of machine learning [24]    
As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what was then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics. [25]  Probabilistic reasoning was also employed, especially in automated medical diagnosis. [26]  : 488 
 However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. [26]  : 488  By 1980, expert systems had come to dominate AI, and statistics was out of favor. [27]  Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval. [26]  : 708–710, 755  Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including Hopfield, Rumelhart, and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation. [26]  : 25 
 Machine learning (ML), reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory. [27] 
 The difference between ML and AI is frequently misunderstood. ML learns and predicts based on passive observations, whereas AI implies an agent interacting with the environment to learn and take actions that maximize its chance of successfully achieving its goals. [28] 
 As of 2020, many sources continue to assert that ML remains a subfield of AI. [29]   [30]   [27]  Others have the view that not all ML is part of AI, but only an 'intelligent subset' of ML should be considered AI. [5]   [31]   [32] 
Data mining[edit]
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.
Optimization[edit]
Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). [33] 
Generalization[edit]
The difference between optimization and machine learning arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples. Characterizing the generalization of various learning algorithms is an active topic of current research, especially for deep learning algorithms.
Statistics[edit]
Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns. [34]  According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. [35]  He also suggested the term data science as a placeholder to call the overall field. [35] 
  Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model, [29]  wherein "algorithmic model" means more or less the machine learning algorithms like Random Forest.
 Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning. [30] 
Physics[edit]
Analytical and computational techniques derived from statistical physics of disordered systems, can be extended to large-scale problems, including machine learning, e.g., to analyze the weight space of deep neural networks. [36]  Statistical physics is thus finding applications in the area of medical diagnostics. [37] 
 Theory[edit]
A core objective of a learner is to generalize from its experience. [5]   [31]  Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.
 The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory via the Probably Approximately Correct Learning (PAC) model. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error.
 For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer. [32] 
 In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results: Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.
Approaches[edit]
Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on the nature of the "signal" or "feedback" available to the learning system:
  Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
 Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
 Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize. [5]   
Supervised learning[edit]
      A support-vector machine is a supervised learning model that divides the data into regions separated by a linear boundary. Here, the linear boundary divides the black circles from the white.  
Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. [38]  The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. [39]  An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task. [20] 
 Types of supervised-learning algorithms include active learning, classification and regression. [28]  Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email.
  Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification.
Unsupervised learning[edit]
Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics, such as finding the probability density function. [40]  Though unsupervised learning encompasses other domains involving summarizing and explaining data features.
 Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity.
Semi-supervised learning[edit]
Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy.
 In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets. [41] 
Reinforcement learning[edit]
Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In machine learning, the environment is typically represented as a Markov decision process (MDP). Many reinforcements learning algorithms use dynamic programming techniques. [42]  Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent.
Dimensionality reduction[edit]
Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables. [43]  In other words, it is a process of reducing the dimension of the feature set, also called the "number of features". Most of the dimensionality reduction techniques can be considered as either feature elimination or extraction. One of the popular methods of dimensionality reduction is principal component analysis (PCA). PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D). This results in a smaller dimension of data (2D instead of 3D), while keeping all original variables in the model without changing the data. [44] 
The manifold hypothesis proposes that high-dimensional data sets lie along low-dimensional manifolds, and many dimensionality reduction techniques make this assumption, leading to the area of manifold learning and manifold regularization.
Other types[edit]
Other approaches have been developed which don't fit neatly into this three-fold categorization, and sometimes more than one is used by the same machine learning system. For example, topic modeling, meta-learning. [45] 
 As of 2022, deep learning is the dominant approach for much ongoing work in the field of machine learning. [11] 
Self-learning[edit]
Self-learning, as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning, named crossbar adaptive array (CAA). [46]  It is learning with no external rewards and no external teacher advice. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion. [47] 
The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine: 
 in situation s perform action a 
receive consequence situation s' 
compute emotion of being in consequence situation v(s') 
update crossbar memory w'(a,s) = w(a,s) + v(s')  
It is a system with only one input, situation, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is the behavioral environment where it behaves, and the other is the genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal-seeking behavior, in an environment that contains both desirable and undesirable situations. [48] 
Feature learning[edit]
Several learning algorithms aim at discovering better representations of the inputs provided during training. [49]  Classic examples include principal component analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task.
 Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization  [50]  and various forms of clustering. [51]   [52]   [53] 
  Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors. [54]  Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data. [55] 
 Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.
Sparse dictionary learning[edit]
Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately. [56]  A popular heuristic method for sparse dictionary learning is the K-SVD algorithm. Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine the class to which a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot. [57] 
Anomaly detection[edit]
In data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. [58]  Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are referred to as outliers, novelties, noise, deviations and exceptions. [59] 
 In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts of inactivity. This pattern does not adhere to the common statistical definition of an outlier as a rare object. Many outlier detection methods (in particular, unsupervised algorithms) will fail on such data unless aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns. [60] 
 Three broad categories of anomaly detection techniques exist. [61]  Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit the least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set and then test the likelihood of a test instance to be generated by the model.
Robot learning[edit]
 Robot learning is inspired by a multitude of machine learning methods, starting from supervised learning, reinforcement learning, [62]   [63]  and finally meta-learning (e.g. MAML).
Association rules[edit]
Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness". [64] 
 Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. [65]  Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems.
 Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. [66]  For example, the rule found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions.
 Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions. [67] 
 Inductive logic programming (ILP) is an approach to rule learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs.
 Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting. [68]   [69]   [70]  Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples. [71]  The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set.
Models[edit]
Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems.
Artificial neural networks[edit]
      An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another.  
Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.
 An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times.
 The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.
  Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition. [72] 
Decision trees[edit]
      A decision tree showing survival probability of passengers on the Titanic   
Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels, and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision-making.
Support-vector machines[edit]
Support-vector machines (SVMs), also known as support-vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category. [73]  An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.
Regression analysis[edit]
      Illustration of linear regression on a data set  
Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel [74] ), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space.
Bayesian networks[edit]
      A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet.  
A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.
Gaussian processes[edit]
      An example of Gaussian Process Regression (prediction) compared with other regression models [75]    
A Gaussian process is a stochastic process in which every finite collection of the random variables in the process has a multivariate normal distribution, and it relies on a pre-defined covariance function, or kernel, that models how pairs of points relate to each other depending on their locations.
 Given a set of observed points, or input–output examples, the distribution of the (unobserved) output of a new point as function of its input data, can be directly computed by looking like the observed points and the covariances between those points and the new, unobserved point.
 Gaussian processes are popular surrogate models in Bayesian optimization used to do hyperparameter optimization.
Genetic algorithms[edit]
A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s. [76]   [77]  Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms. [78] 
Training models[edit]
Typically, machine learning models require a high quantity of reliable data in order for the models to perform accurate predictions. When training a machine learning model, machine learning engineers need to target and collect a large and representative sample of data. Data from the training set can be as varied as a corpus of text, a collection of images, sensor data, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Trained models derived from biased or non-evaluated data can result in skewed or undesired predictions. Bias models may result in detrimental outcomes thereby furthering the negative impacts on society or objectives. Algorithmic bias is a potential result of data not being fully prepared for training. Machine learning ethics is becoming a field of study and notably be integrated within machine learning engineering teams.
Federated learning[edit]
Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google. [79] 
Applications[edit]
There are many applications for machine learning, including:
  Agriculture 
 Anatomy 
 Adaptive website 
 Affective computing 
 Astronomy 
 Automated decision-making 
 Banking 
 Behaviorism 
 Bioinformatics 
 Brain–machine interfaces 
 Cheminformatics 
 Citizen Science 
 Climate Science 
 Computer networks 
 Computer vision 
 Credit-card fraud detection
 Data quality 
 DNA sequence classification
 Economics 
 Financial market analysis [80]  
 General game playing 
 Handwriting recognition 
 Information retrieval 
 Insurance 
 Internet fraud detection
 Knowledge graph embedding 
 Linguistics 
 Machine learning control 
 Machine perception 
 Machine translation 
 Marketing 
 Medical diagnosis 
 Natural language processing 
 Natural language understanding 
 Online advertising 
 Optimization 
 Recommender systems 
 Robot locomotion 
 Search engines 
 Sentiment analysis 
 Sequence mining 
 Software engineering 
 Speech recognition 
 Structural health monitoring 
 Syntactic pattern recognition 
 Telecommunication 
 Theorem proving 
 Time-series forecasting 
 User behavior analytics  
In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&amp;T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million. [81]  Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly. [82]  In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis. [83]  In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software. [84]  In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists. [85]  In 2019 Springer Nature published the first research book created using machine learning. [86]  In 2020, machine learning technology was used to help make diagnoses and aid researchers in developing a cure for COVID-19. [87]  Machine learning was recently applied to predict the pro-environmental behavior of travelers. [88]  Recently, machine learning technology was also applied to optimize smartphone's performance and thermal behavior based on the user's interaction with the phone. [89]   [90]   [91] 
Limitations[edit]
Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results. [92]   [93]   [94]  Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems. [95] 
 In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision. [96]  Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested. [97]   [98] 
 Machine learning has been used as a strategy to update the evidence related to a systematic review and increased reviewer burden related to the growth of biomedical literature. While it has improved with training sets, it has not yet developed sufficiently to reduce the workload burden without limiting the necessary sensitivity for the findings research themselves. [99] 
Bias[edit]
Machine learning approaches in particular can suffer from different data biases. A machine learning system trained specifically on current customers may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the constitutional and unconscious biases already present in society. [100]  Language models learned from data have been shown to contain human-like biases. [101]   [102]  Machine learning systems used for criminal risk assessment have been found to be biased against black people. [103]   [104]  In 2015, Google photos would often tag black people as gorillas, [105]  and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all. [106]  Similar issues with recognizing non-white people have been found in many other systems. [107]  In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language. [108]  Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains. [109]  Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There's nothing artificial about AI...It's inspired by people, it's created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility." [110] 
Explainability[edit]
Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI. It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision. By refining the mental models of users of AI-powered systems and dismantling their misconceptions, XAI promises to help users perform more effectively. XAI may be an implementation of the social right to explanation.
Overfitting[edit]
      The blue line could be an example of overfitting a linear function due to random noise.  
Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data but penalizing the theory in accordance with how complex the theory is. [10] 
Other limitations and vulnerabilities[edit]
Learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. [111]  A real-world example is that, unlike humans, current image classifiers often don't primarily make judgments from the spatial relationship between components of the picture, and they learn relationships between pixels that humans are oblivious to, but that still correlate with images of certain types of real objects. Modifying these patterns on a legitimate image can result in "adversarial" images that the system misclassifies. [112]   [113] 
 Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. Some systems are so brittle that changing a single adversarial pixel predictably induces misclassification.[ citation needed ] Machine learning models are often vulnerable to manipulation and/or evasion via adversarial machine learning. [114] 
 Researchers have demonstrated how backdoors can be placed undetectably into classifying (e.g., for categories "spam" and well-visible "not spam" of posts) machine learning models which are often developed and/or trained by third parties. Parties can change the classification of any input, including in cases for which a type of data/software transparency is provided, possibly including white-box access. [115]   [116]   [117] 
Model assessments[edit]
Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy. [118] 
 In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC). [119] 
Ethics[edit]
Machine learning poses a host of ethical questions. Systems that are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices. [120]  For example, in 1988, the UK's Commission for Racial Equality found that St. George's Medical School had been using a computer program trained from data of previous admissions staff and this program had denied nearly 60 candidates who were found to be either women or had non-European sounding names. [100]  Using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants by similarity to previous successful applicants. [121]   [122]  Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning.
 AI can be well-equipped to make decisions in technical fields, which rely heavily on data and historical information. These decisions rely on the objectivity and logical reasoning. [123]  Because human languages contain biases, machines trained on language  corpora  will necessarily also learn these biases. [124]   [125] 
 Other forms of ethical challenges, not related to personal biases, are seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. [126]  This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increase profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is potential for machine learning in health care to provide professionals an additional tool to diagnose, medicate, and plan recovery paths for patients, but this requires these biases to be mitigated. [127] 
Hardware[edit]
Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units. [128]  By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI. [129]  OpenAI estimated the hardware computing used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months. [130]   [131] 
Neuromorphic/Physical Neural Networks[edit]
A physical neural network or Neuromorphic computer is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse. [132]   [133] 
Embedded Machine Learning[edit]
 Embedded Machine Learning is a sub-field of machine learning, where the machine learning model is run on embedded systems with limited computing resources such as wearable computers, edge devices and microcontrollers. [134]   [135]   [136]  Running machine learning model in embedded devices removes the need for transferring and storing data on cloud servers for further processing, henceforth, reducing data breaches and privacy leaks happening because of transferring data, and also minimizes theft of intellectual properties, personal data and business secrets. Embedded Machine Learning could be applied through several techniques including hardware acceleration, [137]   [138]  using approximate computing, [139]  optimization of machine learning models and many more. [140]   [141] 
Software[edit]
 Software suites containing a variety of machine learning algorithms include the following:
Free and open-source software[edit]
  Caffe 
 Deeplearning4j 
 DeepSpeed 
 ELKI 
 Google JAX 
 Infer.NET 
 Keras 
 Kubeflow 
 LightGBM 
 Mahout 
 Mallet 
 Microsoft Cognitive Toolkit 
 ML.NET 
 mlpack 
MLFlow
 MXNet 
 Neural Lab 
 OpenNN 
 Orange 
 pandas (software) 
 ROOT (TMVA with ROOT)
 scikit-learn 
 Shogun 
 Spark MLlib 
 SystemML 
 TensorFlow 
 Torch / PyTorch 
 Weka / MOA 
 XGBoost 
 Yooreeka  
Proprietary software with free and open-source editions[edit]
  KNIME 
 RapidMiner  
Proprietary software[edit]
  Amazon Machine Learning 
 Angoss KnowledgeSTUDIO
 Azure Machine Learning 
Ayasdi
 IBM Watson Studio 
 Google Cloud Vertex AI 
 Google Prediction API 
 IBM SPSS Modeler 
 KXEN Modeler 
 LIONsolver 
 Mathematica 
 MATLAB 
 Neural Designer 
 NeuroSolutions 
 Oracle Data Mining 
 Oracle AI Platform Cloud Service 
 PolyAnalyst 
 RCASE 
 SAS Enterprise Miner 
 SequenceL 
 Splunk 
 STATISTICA Data Miner 
Journals[edit]
  Journal of Machine Learning Research 
 Machine Learning 
 Nature Machine Intelligence 
 Neural Computation 
IEEE Transactions on Pattern Analysis and Machine Intelligence 
Conferences[edit]
  AAAI Conference on Artificial Intelligence 
 Association for Computational Linguistics (ACL) 
 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 
 International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB) 
 International Conference on Machine Learning (ICML) 
 International Conference on Learning Representations (ICLR) 
 International Conference on Intelligent Robots and Systems (IROS) 
 Conference on Knowledge Discovery and Data Mining (KDD) 
 Conference on Neural Information Processing Systems (NeurIPS)  
See also[edit]
  Automated machine learning – Process of automating the application of machine learning
 Big data – Information assets characterized by high volume, velocity, and variety
 Differentiable programming – Programming paradigm
 List of important publications in machine learning 
 List of datasets for machine-learning research  
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Sources[edit]
   Domingos, Pedro (September 22, 2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. ISBN  978-0465065707 . 
  Nilsson, Nils (1998).  Artificial Intelligence: A New Synthesis . Morgan Kaufmann. ISBN  978-1-55860-467-4 . Archived from the original on 26 July 2020. Retrieved 18 November 2019. 
  Russell, Stuart J.; Norvig, Peter (2003),  Artificial Intelligence: A Modern Approach  (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN  0-13-790395-2  .
 Poole, David; Mackworth, Alan; Goebel, Randy (1998).  Computational Intelligence: A Logical Approach . New York: Oxford University Press. ISBN  978-0-19-510270-3 . Archived from the original on 26 July 2020. Retrieved 22 August 2020.  
Further reading[edit]
 Nils J. Nilsson,  Introduction to Machine Learning Archived 2019-08-16 at the Wayback Machine .
 Trevor Hastie, Robert Tibshirani and Jerome H. Friedman (2001).  The Elements of Statistical Learning Archived 2013-10-27 at the Wayback Machine , Springer. ISBN 0-387-95284-5.
 Pedro Domingos (September 2015),  The Master Algorithm , Basic Books, ISBN 978-0-465-06570-7 
Ian H. Witten and Eibe Frank (2011). Data Mining: Practical machine learning tools and techniques Morgan Kaufmann, 664pp., ISBN 978-0-12-374856-0.
Ethem Alpaydin (2004). Introduction to Machine Learning, MIT Press, ISBN 978-0-262-01243-0.
 David J. C. MacKay.  Information Theory, Inference, and Learning Algorithms Archived 2016-02-17 at the Wayback Machine  Cambridge: Cambridge University Press, 2003. ISBN 0-521-64298-1 
 Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN 0-471-05669-3.
 Christopher Bishop (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN 0-19-853864-2.
Stuart Russell &amp; Peter Norvig, (2009).  Artificial Intelligence – A Modern Approach Archived 2011-02-28 at the Wayback Machine . Pearson, ISBN 9789332543515.
 Ray Solomonoff, An Inductive Inference Machine, IRE Convention Record, Section on Information Theory, Part 2, pp., 56–62, 1957.
 Ray Solomonoff,  An Inductive Inference Machine Archived 2011-04-26 at the Wayback Machine  A privately circulated report from the 1956 Dartmouth Summer Research Conference on AI.
Kevin P. Murphy (2021).  Probabilistic Machine Learning: An Introduction Archived 2021-04-11 at the Wayback Machine , MIT Press. 
External links[edit]
    Quotations related to Machine learning at Wikiquote
 International Machine Learning Society 
 mloss is an academic database of open-source machine learning software.
Machine learning - Wikipedia
https://en.wikipedia.org/wiki/Machine_learning
https://www.coursera.org/learn/machine-learning
Supervised Machine Learning: Regression and Classification
2022-09-03
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<div><div><div><div><h2>About this Course</h2><div><div><p>Flexible deadlines</p><p>Reset deadlines in accordance to your schedule.</p></div><div><p>Shareable Certificate</p><p>Earn a Certificate upon completion</p></div><div><p>100% online</p><p>Start instantly and learn at your own schedule.</p></div><div><p>Beginner Level</p><div><p>Basic coding (for loops, functions, if/else statements) &amp; high school-level math (arithmetic, algebra)</p><p>Other math concepts will be explained</p></div></div><div><p>Approx. 33 hours to complete</p></div></div><div><h2>What you will learn</h2><ul><li><div><p>Build machine learning models in Python using popular machine learning libraries NumPy &amp; scikit-learn</p></div></li><li><div><p>Build &amp; train supervised machine learning models for prediction &amp; binary classification tasks, including linear regression &amp; logistic regression</p></div></li></ul></div><div><h2>Skills you will gain</h2><ul><li>Regularization to Avoid Overfitting</li><li>Gradient Descent</li><li>Supervised Learning</li><li>Linear Regression</li><li>Logistic Regression for Classification</li></ul></div></div><div><div><p>Flexible deadlines</p><p>Reset deadlines in accordance to your schedule.</p></div><div><p>Shareable Certificate</p><p>Earn a Certificate upon completion</p></div><div><p>100% online</p><p>Start instantly and learn at your own schedule.</p></div><div><p>Beginner Level</p><div><p>Basic coding (for loops, functions, if/else statements) &amp; high school-level math (arithmetic, algebra)</p><p>Other math concepts will be explained</p></div></div><div><p>Approx. 33 hours to complete</p></div></div></div><div><p></p><h2>Instructors</h2><p></p></div><div><p></p><h2>Offered by</h2><p></p><div><div><a href="/deeplearning-ai"><div><p></p></div></a></div><div><a href="/stanford"><div><p></p></div></a></div></div></div><div><div><div><p></p></div><div><p>To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood.</p><p>— Felipe M.</p></div></div><ul><li></li><li></li><li></li></ul></div><div><h3>Other courses in this Specialization</h3></div><div><div><h2>Syllabus - What you will learn from this course</h2></div><div><div><h3>Week 1: Introduction to Machine Learning</h3><div><p>20 videos (Total 147 min)</p></div></div><div><h3>Week 2: Regression with multiple input variables</h3></div><div><h3>Week 3: Classification</h3><div><p>11 videos (Total 98 min), 1 reading, 5 quizzes</p></div></div></div></div><div><h2>Reviews</h2><div><div><ul><li><p>5 stars</p><p>91.72%</p></li><li><p>4 stars</p><p>7.22%</p></li><li><p>3 stars</p><p>0.63%</p></li><li><p>2 stars</p><p>0.19%</p></li><li><p>1 star</p><p>0.22%</p></li></ul></div><div><h3>TOP REVIEWS FROM SUPERVISED MACHINE LEARNING: REGRESSION AND CLASSIFICATION </h3><div><p>by APSep 3, 2022</p><p>A perfect course for beginners who are seeking to learn machine learning. I want to thank Mr. Andrew Ng and his team for this wonderful course. Thank you so much for providing this quality course.</p></div><div><p>by AMJul 16, 2022</p><p>It is the Best Course for Supervised Machine Learning!</p><p>Andrew Ng Sir has been like always has such important &amp; difficult concepts of Supervised ML with such ease and great examples, Just amazing!</p></div><div><p>by KYSep 8, 2022</p><p>Professor Andrew can explain complex knowledge clearly. The Python lab can help learner to understand algorithm. The course is more valuable. I am excited to learn the next course for advanced ML.</p></div><div><p>by KRSep 26, 2022</p><p>While I think this course is fantastic I really wish there was some place you cuold ask questions or engage in discussion. If I missed that then my apologies. Overall absolutely worth the time though.</p></div><p><a href="/learn/machine-learning/reviews">View all reviews</a></p></div></div></div><div><div><p></p><h2>About the Machine Learning Specialization</h2><p></p></div><div><p></p></div></div><div><div><h2>Start Learning Today</h2><h2></h2><div><div><p>This Course Plus the Full Specialization</p></div><div><p>Self-Paced Learning Option</p></div><div><p>Graded Assignments with Peer Feedback</p></div><div><p>Graded Quizzes with Feedback</p></div><div><p>Graded Programming Assignments</p></div></div></div><div><div><p>Shareable on</p><div><p></p></div></div><div><p>You can share your Course Certificates in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.</p></div></div></div><div><ul><li><div><p>When will I have access to the lectures and assignments?</p></div></li><li><div><p>What will I get if I subscribe to this Specialization?</p></div></li><li><div><p>Is financial aid available?</p></div></li></ul><p>More questions? Visit the <a href="https://learner.coursera.help/hc/">Learner Help Center</a>.</p></div></div>||||I|||| Skip to Main Content
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 Supervised Machine Learning: Regression and Classification

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 About this Course

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 In the first course of the Machine Learning Specialization, you will:

 • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
 • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression
 
 The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. 
 
 This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
 
 This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. 
 
 It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)
 
 By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

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 Flexible deadlines
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 Coursera Labs
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 Course 1 of 3 in the
 Machine Learning Specialization
 Beginner Level

 Basic coding (for loops, functions, if/else statements) & high school-level math (arithmetic, algebra)

 Other math concepts will be explained

 Approx. 33 hours to complete
 English
 Subtitles: English

 What you will learn

 * Build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn

 * Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression

 Skills you will gain

 * Regularization to Avoid Overfitting
 * Gradient Descent
 * Supervised Learning
 * Linear Regression
 * Logistic Regression for Classification
 Flexible deadlines
 Reset deadlines in accordance to your schedule.
 Shareable Certificate
 Earn a Certificate upon completion
 100% online
 Start instantly and learn at your own schedule.
 Coursera Labs
 Includes hands on learning projects.
 Learn more about Coursera Labs
 Course 1 of 3 in the
 Machine Learning Specialization
 Beginner Level

 Basic coding (for loops, functions, if/else statements) & high school-level math (arithmetic, algebra)

 Other math concepts will be explained

 Approx. 33 hours to complete
 English
 Subtitles: English

 Instructors

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 DeepLearning.AI

 DeepLearning.AI is an education technology company that develops a global community of AI talent.

 DeepLearning.AI's expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future.

 Stanford University

 The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.

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 When I left for maternity leave, I lost my interior design job. Health courses on Coursera helped me become a registered nurse and start a new path in life.

 — Dimitra T.

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 Syllabus - What you will learn from this course

 Content Rating 99 % (75,254 ratings)
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 Week
 1
 Week 1
 7 hours to complete

 Week 1: Introduction to Machine Learning

 Welcome to the Machine Learning Specialization! You're joining millions of others who have taken either this or the original course, which led to the founding of Coursera, and has helped millions of other learners, like you, take a look at the exciting world of machine learning!

 7 hours to complete
 20 videos (Total 147 min)
 See All
 20 videos
 Welcome to machine learning! 2m
 Applications of machine learning 4m
 What is machine learning? 5m
 Supervised learning part 1 6m
 Supervised learning part 2 7m
 Unsupervised learning part 1 8m
 Unsupervised learning part 2 3m
 Jupyter Notebooks 4m
 Linear regression model part 1 10m
 Linear regression model part 2 6m
 Cost function formula 9m
 Cost function intuition 15m
 Visualizing the cost function 8m
 Visualization examples 6m
 Gradient descent 8m
 Implementing gradient descent 9m
 Gradient descent intuition 7m
 Learning rate 9m
 Gradient descent for linear regression 6m
 Running gradient descent 5m
 3 practice exercises
 Practice quiz: Supervised vs unsupervised learning 15m
 Practice quiz: Regression 10m
 Practice quiz: Train the model with gradient descent 10m
 Week
 2
 Week 2
 10 hours to complete

 Week 2: Regression with multiple input variables

 This week, you'll extend linear regression to handle multiple input features. You'll also learn some methods for improving your model's training and performance, such as vectorization, feature scaling, feature engineering and polynomial regression. At the end of the week, you'll get to practice implementing linear regression in code.

 10 hours to complete
 10 videos (Total 66 min)
 See All
 10 videos
 Multiple features 9m
 Vectorization part 1 6m
 Vectorization part 2 6m
 Gradient descent for multiple linear regression 7m
 Feature scaling part 1 6m
 Feature scaling part 2 7m
 Checking gradient descent for convergence 5m
 Choosing the learning rate 6m
 Feature engineering 3m
 Polynomial regression 5m
 2 practice exercises
 Practice quiz: Multiple linear regression 15m
 Practice quiz: Gradient descent in practice 30m
 Week
 3
 Week 3
 16 hours to complete

 Week 3: Classification

 This week, you'll learn the other type of supervised learning, classification. You'll learn how to predict categories using the logistic regression model. You'll learn about the problem of overfitting, and how to handle this problem with a method called regularization. You'll get to practice implementing logistic regression with regularization at the end of this week!

 16 hours to complete
 11 videos (Total 98 min) , 1 reading , 5 quizzes
 See All
 11 videos
 Motivations 9m
 Logistic regression 9m
 Decision boundary 10m
 Cost function for logistic regression 11m
 Simplified Cost Function for Logistic Regression 5m
 Gradient Descent Implementation 6m
 The problem of overfitting 11m
 Addressing overfitting 8m
 Cost function with regularization 9m
 Regularized linear regression 8m
 Regularized logistic regression 5m
 1 reading
 Acknowledgments 2m
 4 practice exercises
 Practice quiz: Classification with logistic regression 30m
 Practice quiz: Cost function for logistic regression 30m
 Practice quiz: Gradient descent for logistic regression 30m
 Practice quiz: The problem of overfitting 30m

 Reviews

 4.9
 1651 reviews
 * 5 stars
 91.72%
 * 4 stars
 7.22%
 * 3 stars
 0.63%
 * 2 stars
 0.19%
 * 1 star
 0.22%

 TOP REVIEWS FROM SUPERVISED MACHINE LEARNING: REGRESSION AND CLASSIFICATION

 by AP Sep 3, 2022

 A perfect course for beginners who are seeking to learn machine learning. I want to thank Mr. Andrew Ng and his team for this wonderful course. Thank you so much for providing this quality course.

 by AM Jul 16, 2022

 It is the Best Course for Supervised Machine Learning!

 Andrew Ng Sir has been like always has such important & difficult concepts of Supervised ML with such ease and great examples, Just amazing!

 by KY Sep 8, 2022

 Professor Andrew can explain complex knowledge clearly. The Python lab can help learner to understand algorithm. The course is more valuable. I am excited to learn the next course for advanced ML.

 by KR Sep 26, 2022

 While I think this course is fantastic I really wish there was some place you cuold ask questions or engage in discussion. If I missed that then my apologies. Overall absolutely worth the time though.

 View all reviews

 About the Machine Learning Specialization

 The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

 This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

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About this Course   Flexible deadlines Reset deadlines in accordance to your schedule.   Shareable Certificate Earn a Certificate upon completion   100% online Start instantly and learn at your own schedule.   Beginner Level  Basic coding (for loops, functions, if/else statements) &amp; high school-level math (arithmetic, algebra) Other math concepts will be explained    Approx. 33 hours to complete    What you will learn    Build machine learning models in Python using popular machine learning libraries NumPy &amp; scikit-learn     Build &amp; train supervised machine learning models for prediction &amp; binary classification tasks, including linear regression &amp; logistic regression      Skills you will gain  Regularization to Avoid Overfitting Gradient Descent Supervised Learning Linear Regression Logistic Regression for Classification      Flexible deadlines Reset deadlines in accordance to your schedule.   Shareable Certificate Earn a Certificate upon completion   100% online Start instantly and learn at your own schedule.   Beginner Level  Basic coding (for loops, functions, if/else statements) &amp; high school-level math (arithmetic, algebra) Other math concepts will be explained    Approx. 33 hours to complete       Instructors       Offered by                             To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood. — Felipe M.             Other courses in this Specialization    Syllabus - What you will learn from this course    Week 1: Introduction to Machine Learning  20 videos (Total 147 min)    Week 2: Regression with multiple input variables   Week 3: Classification  11 videos (Total 98 min), 1 reading, 5 quizzes      Reviews     5 stars 91.72%   4 stars 7.22%   3 stars 0.63%   2 stars 0.19%   1 star 0.22%     TOP REVIEWS FROM SUPERVISED MACHINE LEARNING: REGRESSION AND CLASSIFICATION   by APSep 3, 2022 A perfect course for beginners who are seeking to learn machine learning. I want to thank Mr. Andrew Ng and his team for this wonderful course. Thank you so much for providing this quality course.   by AMJul 16, 2022 It is the Best Course for Supervised Machine Learning! Andrew Ng Sir has been like always has such important &amp; difficult concepts of Supervised ML with such ease and great examples, Just amazing!   by KYSep 8, 2022 Professor Andrew can explain complex knowledge clearly. The Python lab can help learner to understand algorithm. The course is more valuable. I am excited to learn the next course for advanced ML.   by KRSep 26, 2022 While I think this course is fantastic I really wish there was some place you cuold ask questions or engage in discussion. If I missed that then my apologies. Overall absolutely worth the time though.   View all reviews         About the Machine Learning Specialization           Start Learning Today     This Course Plus the Full Specialization   Self-Paced Learning Option   Graded Assignments with Peer Feedback   Graded Quizzes with Feedback   Graded Programming Assignments      Shareable on       You can share your Course Certificates in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.        When will I have access to the lectures and assignments?     What will I get if I subscribe to this Specialization?     Is financial aid available?    More questions? Visit the Learner Help Center.
Supervised Machine Learning: Regression and Classification
https://www.coursera.org/learn/machine-learning
https://www.expert.ai/blog/machine-learning-definition/
What Is Machine Learning? A Definition.
2022-03-13
None
<div><div><p></p>
<p>The robot-depicted world of our not-so-distant future relies heavily on our ability to deploy artificial intelligence (AI) successfully. However, transforming machines into thinking devices is not as easy as it may seem. Strong AI can only be achieved with machine learning (ML) to help machines understand as humans do.</p>
<p>Machine learning can be confusing, so it is important that we begin by clearly defining the term:</p>
<blockquote><p>Machine learning is an application of AI that enables systems to learn and improve from experience without being explicitly programmed. Machine learning focuses on developing computer programs that can access data and use it to learn for themselves.</p>
</blockquote>
<h2>How Does Machine Learning Work?</h2>
<p>Similar to how the human brain gains knowledge and understanding, machine learning relies on input, such as training data or knowledge graphs, to understand entities, domains and the connections between them. With entities defined, deep learning can begin.</p>
<p>The machine learning process begins with observations or data, such as examples, direct experience or instruction. It looks for patterns in data so it can later make inferences based on the examples provided. The primary aim of ML is to allow computers to learn autonomously without human intervention or assistance and adjust actions accordingly.</p>
<h2>Why Is Machine Learning Important?</h2>
<p>Machine learning as a concept has been around for quite some time. The term “machine learning” was coined by Arthur Samuel, a computer scientist at IBM and a pioneer in AI and computer gaming. Samuel designed a computer program for playing checkers. The more the program played, the more it learned from experience, using algorithms to make predictions.</p>
<p>As a discipline, machine learning explores the analysis and construction of algorithms that can learn from and make predictions on data.</p>
<p>ML has proven valuable because it can solve problems at a speed and scale that cannot be duplicated by the human mind alone. With massive amounts of computational ability behind a single task or multiple specific tasks, machines can be trained to identify patterns in and relationships between input data and automate routine processes.</p>
<ul>
<li><strong>Data Is Key</strong>: The algorithms that drive machine learning are critical to success. ML algorithms build a mathematical model based on sample data, known as “training data,” to make predictions or decisions without being explicitly programmed to do so. This can reveal trends within data that information businesses can use to improve decision making, optimize efficiency and capture actionable data at scale.</li>
<li><strong>AI Is the Goal</strong>: ML provides the foundation for AI systems that automate processes and solve data-based business problems autonomously. It enables companies to replace or augment certain human capabilities. Common machine learning applications you may find in the real world include <a href="https://www.expert.ai/blog/chatbot/">chatbots</a>, self-driving cars and speech recognition.</li>
</ul>
<h2>Machine Learning Is Widely Adopted</h2>
<p>Machine learning is not science fiction. It is already widely used by businesses across all sectors to advance innovation and increase process efficiency. In 2021, <a href="https://www.itprotoday.com/data-analytics-and-data-management/2022-ai-machine-learning-and-data-trends-watch">41% of companies accelerated their rollout of AI</a> as a result of the pandemic. These newcomers are joining the <a href="https://venturebeat.com/2022/03/12/executives-discuss-top-challenges-in-deploying-ai-and-how-to-solve-them/">31% of companies that already have AI in production</a> or are actively piloting AI technologies.</p>
<ul>
<li><strong>Data security</strong>: Machine learning models can identify data security vulnerabilities before they can turn into breaches. By looking at past experiences, machine learning models can predict future high-risk activities so risk can be proactively mitigated.</li>
<li><strong>Finance</strong>: Banks, trading brokerages and fintech firms use machine learning algorithms to automate trading and to provide financial advisory services to investors. Bank of America is using a chatbot, Erica, to automate customer support.</li>
<li><strong>Healthcare</strong>: ML is used to analyze massive healthcare data sets to accelerate discovery of treatments and cures, improve patient outcomes, and automate routine processes to prevent human error. For example, IBM’s Watson uses <a href="https://www.compunneldigital.com/blog/ai-in-healthcare-top-5-real-world-examples/">data mining to provide physicians data</a> they can use to personalize patient treatment.</li>
<li><strong>Fraud detection</strong>: AI is being used in the financial and banking sector to autonomously analyze large numbers of transactions to uncover fraudulent activity in real time. Technology services firm Capgemini claims that fraud detection systems using machine learning and analytics <a href="https://securityboulevard.com/2022/02/how-ai-is-becoming-integral-to-fintech-security/">minimize fraud investigation time by 70% and improve detection accuracy by 90%</a>.</li>
<li><strong>Retail</strong>: AI researchers and developers are using ML algorithms to develop AI recommendation engines that offer relevant product suggestions based on buyers’ past choices, as well as historical, geographic and demographic data.</li>
</ul>
<h2>Training Methods for Machine Learning Differ</h2>
<p>Machine learning offers clear benefits for AI technologies. But which machine learning approach is right for your organization? There are many to ML training methods to choose from including:</p>
<ul>
<li>supervised learning</li>
<li>unsupervised learning</li>
<li>semi-supervised learning</li>
</ul>
<p>Let’s see what each has to offer.</p>
<h3>Supervised Learning: More Control, Less Bias</h3>
<p>Supervised machine learning algorithms apply what has been learned in the past to new data using labeled examples to predict future events. By analyzing a known training dataset, the learning algorithm produces an inferred function to predict output values. The system can provide targets for any new input after sufficient training. It can also compare its output with the correct, intended output to find errors and modify the model accordingly.</p>
<h3>Unsupervised Learning: Speed and Scale</h3>
<p>Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. At no point does the system know the correct output with certainty. Instead, it draws inferences from datasets as to what the output should be.</p>
<h3>Reinforcement Learning: Rewards Outcomes</h3>
<p>Reinforcement machine learning algorithms are a learning method that interacts with its environment by producing actions and discovering errors or rewards. The most relevant characteristics of reinforcement learning are trial and error search and delayed reward. This method allows machines and software agents to automatically determine the ideal behavior within a specific context to maximize its performance. Simple reward feedback — known as the reinforcement signal — is required for the agent to learn which action is best.</p>
<h2>Machine Learning Is Not Perfect</h2>
<p>It is important to understand what machine learning can and cannot do. As useful as it is in automating the transfer of human intelligence to machines, it is far from a perfect solution to your data-related issues. Consider the following shortcomings before you dive too deep into the ML pool:</p>
<ul>
<li><strong>Machine learning is not based in knowledge.</strong> Contrary to popular belief, machine learning cannot attain human-level intelligence. Machines are driven by data, not human knowledge. As a result, “intelligence” is dictated by the volume of data you have to train it with.</li>
<li><strong>Machine learning models are difficult to train.</strong> Eighty-one percent of data scientists admit that <a href="https://www.expert.ai/blog/overcoming-training-data-roadblocks-with-hybrid-ai/?">training AI with data is more difficult than expected</a>. It takes time and resources to train machines. Massive data sets are needed to create data models, and the process involves manually pre-tagging and categorizing data sets. This resource drain can create latency and bottlenecks in advancing ML initiatives.</li>
<li><strong>Machine learning is prone to data issues.</strong> Ninety-six percent of companies have <a href="https://content.alegion.com/dimensional-researchs-survey">experienced training-related problems with data quality, data labeling and building model confidence</a>. Those training-related problems are a key reason why seventy-eight percent of ML projects stall prior to deployment. This has created an extraordinarily high threshold for ML success.</li>
<li><strong>Machine learning is often biased.</strong> Machine learning systems are known for operating in a black box, meaning you have no visibility into how the machine learns and makes decisions. Thus, if you identify an instance of bias, there is no way to identify what caused it. Your only recourse is to retrain the algorithm with additional data, but that is no guarantee to resolve the issue.</li>
</ul>
<h2>The Future of Machine Learning: Hybrid AI</h2>
<p>For all of its shortcomings, machine learning is still critical to the success of AI. This success, however, will be contingent upon another approach to AI that counters its weaknesses, like the “black box” issue that occurs when machines learn unsupervised. That approach is symbolic AI, or a rule-based methodology toward processing data. A symbolic approach uses a knowledge graph, which is an open box, to define concepts and semantic relationships.</p>
<p>Together, ML and symbolic AI form hybrid AI, an approach that helps AI understand language, not just data. With more insight into what was learned and why, this powerful approach is transforming how data is used across the enterprise.</p>
</div><div><div><h6>Augmenting Machine Learning in a Hybrid AI</h6><p>Discover what differentiates hybrid AI from traditional machine learning and how it will transform the way we use our data.</p>
<p><a href="https://www.expert.ai/resource/the-expert-guide-to-hybrid-artificial-intelligence-4-part-series/?">Learn More</a></p></div><figure>
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 Home / Insights & Resources / What Is Machine Learning? A Definition.

 What Is Machine Learning? A Definition.

 [email protected] - 14 March 2022


 The robot-depicted world of our not-so-distant future relies heavily on our ability to deploy artificial intelligence (AI) successfully. However, transforming machines into thinking devices is not as easy as it may seem. Strong AI can only be achieved with machine learning (ML) to help machines understand as humans do.

 Machine learning can be confusing, so it is important that we begin by clearly defining the term:

 Machine learning is an application of AI that enables systems to learn and improve from experience without being explicitly programmed. Machine learning focuses on developing computer programs that can access data and use it to learn for themselves.

 How Does Machine Learning Work?

 Similar to how the human brain gains knowledge and understanding, machine learning relies on input, such as training data or knowledge graphs, to understand entities, domains and the connections between them. With entities defined, deep learning can begin.

 The machine learning process begins with observations or data, such as examples, direct experience or instruction. It looks for patterns in data so it can later make inferences based on the examples provided. The primary aim of ML is to allow computers to learn autonomously without human intervention or assistance and adjust actions accordingly.

 Why Is Machine Learning Important?

 Machine learning as a concept has been around for quite some time. The term “machine learning” was coined by Arthur Samuel, a computer scientist at IBM and a pioneer in AI and computer gaming. Samuel designed a computer program for playing checkers. The more the program played, the more it learned from experience, using algorithms to make predictions.

 As a discipline, machine learning explores the analysis and construction of algorithms that can learn from and make predictions on data.

 ML has proven valuable because it can solve problems at a speed and scale that cannot be duplicated by the human mind alone. With massive amounts of computational ability behind a single task or multiple specific tasks, machines can be trained to identify patterns in and relationships between input data and automate routine processes.

 * Data Is Key: The algorithms that drive machine learning are critical to success. ML algorithms build a mathematical model based on sample data, known as “training data,” to make predictions or decisions without being explicitly programmed to do so. This can reveal trends within data that information businesses can use to improve decision making, optimize efficiency and capture actionable data at scale.
 * AI Is the Goal: ML provides the foundation for AI systems that automate processes and solve data-based business problems autonomously. It enables companies to replace or augment certain human capabilities. Common machine learning applications you may find in the real world include chatbots, self-driving cars and speech recognition.

 Machine Learning Is Widely Adopted

 Machine learning is not science fiction. It is already widely used by businesses across all sectors to advance innovation and increase process efficiency. In 2021, 41% of companies accelerated their rollout of AI as a result of the pandemic. These newcomers are joining the 31% of companies that already have AI in production or are actively piloting AI technologies.

 * Data security: Machine learning models can identify data security vulnerabilities before they can turn into breaches. By looking at past experiences, machine learning models can predict future high-risk activities so risk can be proactively mitigated.
 * Finance: Banks, trading brokerages and fintech firms use machine learning algorithms to automate trading and to provide financial advisory services to investors. Bank of America is using a chatbot, Erica, to automate customer support.
 * Healthcare: ML is used to analyze massive healthcare data sets to accelerate discovery of treatments and cures, improve patient outcomes, and automate routine processes to prevent human error. For example, IBM’s Watson uses data mining to provide physicians data they can use to personalize patient treatment.
 * Fraud detection: AI is being used in the financial and banking sector to autonomously analyze large numbers of transactions to uncover fraudulent activity in real time. Technology services firm Capgemini claims that fraud detection systems using machine learning and analytics minimize fraud investigation time by 70% and improve detection accuracy by 90%.
 * Retail: AI researchers and developers are using ML algorithms to develop AI recommendation engines that offer relevant product suggestions based on buyers’ past choices, as well as historical, geographic and demographic data.

 Training Methods for Machine Learning Differ

 Machine learning offers clear benefits for AI technologies. But which machine learning approach is right for your organization? There are many to ML training methods to choose from including:

 * supervised learning
 * unsupervised learning
 * semi-supervised learning

 Let’s see what each has to offer.

 Supervised Learning: More Control, Less Bias

 Supervised machine learning algorithms apply what has been learned in the past to new data using labeled examples to predict future events. By analyzing a known training dataset, the learning algorithm produces an inferred function to predict output values. The system can provide targets for any new input after sufficient training. It can also compare its output with the correct, intended output to find errors and modify the model accordingly.

 Unsupervised Learning: Speed and Scale

 Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. At no point does the system know the correct output with certainty. Instead, it draws inferences from datasets as to what the output should be.

 Reinforcement Learning: Rewards Outcomes

 Reinforcement machine learning algorithms are a learning method that interacts with its environment by producing actions and discovering errors or rewards. The most relevant characteristics of reinforcement learning are trial and error search and delayed reward. This method allows machines and software agents to automatically determine the ideal behavior within a specific context to maximize its performance. Simple reward feedback — known as the reinforcement signal — is required for the agent to learn which action is best.

 Machine Learning Is Not Perfect

 It is important to understand what machine learning can and cannot do. As useful as it is in automating the transfer of human intelligence to machines, it is far from a perfect solution to your data-related issues. Consider the following shortcomings before you dive too deep into the ML pool:

 * Machine learning is not based in knowledge. Contrary to popular belief, machine learning cannot attain human-level intelligence. Machines are driven by data, not human knowledge. As a result, “intelligence” is dictated by the volume of data you have to train it with.
 * Machine learning models are difficult to train. Eighty-one percent of data scientists admit that training AI with data is more difficult than expected. It takes time and resources to train machines. Massive data sets are needed to create data models, and the process involves manually pre-tagging and categorizing data sets. This resource drain can create latency and bottlenecks in advancing ML initiatives.
 * Machine learning is prone to data issues. Ninety-six percent of companies have experienced training-related problems with data quality, data labeling and building model confidence. Those training-related problems are a key reason why seventy-eight percent of ML projects stall prior to deployment. This has created an extraordinarily high threshold for ML success.
 * Machine learning is often biased. Machine learning systems are known for operating in a black box, meaning you have no visibility into how the machine learns and makes decisions. Thus, if you identify an instance of bias, there is no way to identify what caused it. Your only recourse is to retrain the algorithm with additional data, but that is no guarantee to resolve the issue.

 The Future of Machine Learning: Hybrid AI

 For all of its shortcomings, machine learning is still critical to the success of AI. This success, however, will be contingent upon another approach to AI that counters its weaknesses, like the “black box” issue that occurs when machines learn unsupervised. That approach is symbolic AI, or a rule-based methodology toward processing data. A symbolic approach uses a knowledge graph, which is an open box, to define concepts and semantic relationships.

 Together, ML and symbolic AI form hybrid AI, an approach that helps AI understand language, not just data. With more insight into what was learned and why, this powerful approach is transforming how data is used across the enterprise.

 Augmenting Machine Learning in a Hybrid AI

 Discover what differentiates hybrid AI from traditional machine learning and how it will transform the way we use our data.

 Learn More

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The robot-depicted world of our not-so-distant future relies heavily on our ability to deploy artificial intelligence (AI) successfully. However, transforming machines into thinking devices is not as easy as it may seem. Strong AI can only be achieved with machine learning (ML) to help machines understand as humans do.
Machine learning can be confusing, so it is important that we begin by clearly defining the term:
 Machine learning is an application of AI that enables systems to learn and improve from experience without being explicitly programmed. Machine learning focuses on developing computer programs that can access data and use it to learn for themselves.
How Does Machine Learning Work?
Similar to how the human brain gains knowledge and understanding, machine learning relies on input, such as training data or knowledge graphs, to understand entities, domains and the connections between them. With entities defined, deep learning can begin.
The machine learning process begins with observations or data, such as examples, direct experience or instruction. It looks for patterns in data so it can later make inferences based on the examples provided. The primary aim of ML is to allow computers to learn autonomously without human intervention or assistance and adjust actions accordingly.
Why Is Machine Learning Important?
Machine learning as a concept has been around for quite some time. The term “machine learning” was coined by Arthur Samuel, a computer scientist at IBM and a pioneer in AI and computer gaming. Samuel designed a computer program for playing checkers. The more the program played, the more it learned from experience, using algorithms to make predictions.
As a discipline, machine learning explores the analysis and construction of algorithms that can learn from and make predictions on data.
ML has proven valuable because it can solve problems at a speed and scale that cannot be duplicated by the human mind alone. With massive amounts of computational ability behind a single task or multiple specific tasks, machines can be trained to identify patterns in and relationships between input data and automate routine processes.
 Data Is Key: The algorithms that drive machine learning are critical to success. ML algorithms build a mathematical model based on sample data, known as “training data,” to make predictions or decisions without being explicitly programmed to do so. This can reveal trends within data that information businesses can use to improve decision making, optimize efficiency and capture actionable data at scale.
 AI Is the Goal: ML provides the foundation for AI systems that automate processes and solve data-based business problems autonomously. It enables companies to replace or augment certain human capabilities. Common machine learning applications you may find in the real world include chatbots, self-driving cars and speech recognition.
Machine Learning Is Widely Adopted
Machine learning is not science fiction. It is already widely used by businesses across all sectors to advance innovation and increase process efficiency. In 2021, 41% of companies accelerated their rollout of AI as a result of the pandemic. These newcomers are joining the 31% of companies that already have AI in production or are actively piloting AI technologies.
 Data security: Machine learning models can identify data security vulnerabilities before they can turn into breaches. By looking at past experiences, machine learning models can predict future high-risk activities so risk can be proactively mitigated.
 Finance: Banks, trading brokerages and fintech firms use machine learning algorithms to automate trading and to provide financial advisory services to investors. Bank of America is using a chatbot, Erica, to automate customer support.
 Healthcare: ML is used to analyze massive healthcare data sets to accelerate discovery of treatments and cures, improve patient outcomes, and automate routine processes to prevent human error. For example, IBM’s Watson uses data mining to provide physicians data they can use to personalize patient treatment.
 Fraud detection: AI is being used in the financial and banking sector to autonomously analyze large numbers of transactions to uncover fraudulent activity in real time. Technology services firm Capgemini claims that fraud detection systems using machine learning and analytics minimize fraud investigation time by 70% and improve detection accuracy by 90%.
 Retail: AI researchers and developers are using ML algorithms to develop AI recommendation engines that offer relevant product suggestions based on buyers’ past choices, as well as historical, geographic and demographic data.
Training Methods for Machine Learning Differ
Machine learning offers clear benefits for AI technologies. But which machine learning approach is right for your organization? There are many to ML training methods to choose from including:
supervised learning
unsupervised learning
semi-supervised learning
Let’s see what each has to offer.
Supervised Learning: More Control, Less Bias
Supervised machine learning algorithms apply what has been learned in the past to new data using labeled examples to predict future events. By analyzing a known training dataset, the learning algorithm produces an inferred function to predict output values. The system can provide targets for any new input after sufficient training. It can also compare its output with the correct, intended output to find errors and modify the model accordingly.
Unsupervised Learning: Speed and Scale
Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. At no point does the system know the correct output with certainty. Instead, it draws inferences from datasets as to what the output should be.
Reinforcement Learning: Rewards Outcomes
Reinforcement machine learning algorithms are a learning method that interacts with its environment by producing actions and discovering errors or rewards. The most relevant characteristics of reinforcement learning are trial and error search and delayed reward. This method allows machines and software agents to automatically determine the ideal behavior within a specific context to maximize its performance. Simple reward feedback — known as the reinforcement signal — is required for the agent to learn which action is best.
Machine Learning Is Not Perfect
It is important to understand what machine learning can and cannot do. As useful as it is in automating the transfer of human intelligence to machines, it is far from a perfect solution to your data-related issues. Consider the following shortcomings before you dive too deep into the ML pool:
 Machine learning is not based in knowledge. Contrary to popular belief, machine learning cannot attain human-level intelligence. Machines are driven by data, not human knowledge. As a result, “intelligence” is dictated by the volume of data you have to train it with.
 Machine learning models are difficult to train. Eighty-one percent of data scientists admit that training AI with data is more difficult than expected. It takes time and resources to train machines. Massive data sets are needed to create data models, and the process involves manually pre-tagging and categorizing data sets. This resource drain can create latency and bottlenecks in advancing ML initiatives.
 Machine learning is prone to data issues. Ninety-six percent of companies have experienced training-related problems with data quality, data labeling and building model confidence. Those training-related problems are a key reason why seventy-eight percent of ML projects stall prior to deployment. This has created an extraordinarily high threshold for ML success.
 Machine learning is often biased. Machine learning systems are known for operating in a black box, meaning you have no visibility into how the machine learns and makes decisions. Thus, if you identify an instance of bias, there is no way to identify what caused it. Your only recourse is to retrain the algorithm with additional data, but that is no guarantee to resolve the issue.
The Future of Machine Learning: Hybrid AI
For all of its shortcomings, machine learning is still critical to the success of AI. This success, however, will be contingent upon another approach to AI that counters its weaknesses, like the “black box” issue that occurs when machines learn unsupervised. That approach is symbolic AI, or a rule-based methodology toward processing data. A symbolic approach uses a knowledge graph, which is an open box, to define concepts and semantic relationships.
Together, ML and symbolic AI form hybrid AI, an approach that helps AI understand language, not just data. With more insight into what was learned and why, this powerful approach is transforming how data is used across the enterprise.
   Augmenting Machine Learning in a Hybrid AI Discover what differentiates hybrid AI from traditional machine learning and how it will transform the way we use our data.
 Learn More
What Is Machine Learning? A Definition.
https://www.expert.ai/blog/machine-learning-definition/
https://www.geeksforgeeks.org/machine-learning/
Machine Learning Tutorial
2023-03-31
None
<div><article><div><p></p><p>Machine Learning tutorial covers basic and advanced concepts, specially designed to cater to both students and experienced working professionals.</p><p>This machine learning tutorial helps you gain a solid introduction to the fundamentals of machine learning and explore a wide range of techniques, including supervised, unsupervised, and reinforcement learning.</p><h2>What is Machine Learning?</h2><p>Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.</p><h3><a href="https://www.geeksforgeeks.org/tag/machine-learning/">Recent Articles on Machine Learning</a></h3><div><div><ul> <li><a href="#int">Introduction</a></li><li><a href="#dp">Data and it’s Processing</a></li><li><a href="#su">Supervised Learning</a></li><li><a href="#unsu">Unsupervised Learning</a></li><li><a href="#hmm">Reinforcement Learning</a></li></ul></div><div><ul> <li><a href="#dr">Dimensionality Reduction</a></li><li><a href="#nlp">Natural Language Processing</a></li><li><a href="#mp">Neural Networks</a></li><li><a href="#dp">ML – Deployment</a></li><li><a href="#ap">ML – Applications</a></li><li><a href="#misc">Miscellaneous</a></li></ul></div></div><h2>Features of Machine learning</h2><ul><li>Machine learning is data driven technology. Large amount of data generated by organizations on daily bases. So, by notable relationships in data, organizations makes better decisions.</li><li>Machine can learn itself from past data and automatically improve.</li><li>From the given dataset it detects various patterns on data.</li><li>For the big organizations branding is important and it will become more easy to target relatable customer base.</li><li>It is similar to data mining because it is also deals with the huge amount of data.</li></ul><h2><a></a>Introduction :</h2><div><ol><li><a href="https://www.geeksforgeeks.org/getting-started-machine-learning/">Getting Started with Machine Learning</a></li><li><a href="https://www.geeksforgeeks.org/introduction-machine-learning/">An Introduction to Machine Learning</a></li><li><a href="https://www.geeksforgeeks.org/ml-machine-learning/">What is Machine Learning ?</a></li><li><a href="https://www.geeksforgeeks.org/ml-introduction-data-machine-learning/">Introduction to Data in Machine Learning</a></li><li><a href="https://www.geeksforgeeks.org/demystifying-machine-learning/">Demystifying Machine Learning</a></li><li><a href="https://www.geeksforgeeks.org/machine-learning-introduction/">ML – Applications</a></li><li><a href="https://www.geeksforgeeks.org/best-python-libraries-for-machine-learning/">Best Python libraries for Machine Learning</a></li><li><a href="https://www.geeksforgeeks.org/artificial-intelligence-an-introduction/">Artificial Intelligence | An Introduction</a></li><li><a href="https://www.geeksforgeeks.org/machine-learning-versus-artificial-intelligence/">Machine Learning and Artificial Intelligence</a></li><li><a href="https://www.geeksforgeeks.org/difference-between-machine-learning-and-artificial-intelligence/">Difference between Machine learning and Artificial Intelligence</a></li><li><a href="https://www.geeksforgeeks.org/agents-artificial-intelligence/">Agents in Artificial Intelligence</a></li><li><a href="https://www.geeksforgeeks.org/10-basic-machine-learning-interview-questions/">10 Basic Machine Learning Interview Questions</a></li></ol></div><h2><a></a>Data and It’s Processing:</h2><div><ol><li><a href="https://www.geeksforgeeks.org/ml-introduction-data-machine-learning/">Introduction to Data in Machine Learning</a></li><li><a href="https://www.geeksforgeeks.org/ml-understanding-data-processing/">Understanding Data Processing</a></li><li><a href="https://www.geeksforgeeks.org/python-create-test-datasets-using-sklearn/">Python | Create Test DataSets using Sklearn</a></li><li><a href="https://www.geeksforgeeks.org/python-generate-test-datasets-for-machine-learning/">Python | Generate test datasets for Machine learning</a></li><li><a href="https://www.geeksforgeeks.org/data-preprocessing-machine-learning-python/">Python | Data Preprocessing in Python</a></li><li><a href="https://www.geeksforgeeks.org/data-cleansing-introduction/">Data Cleaning</a></li><li><a href="https://www.geeksforgeeks.org/ml-feature-scaling-part-1/">Feature Scaling – Part 1</a></li><li><a href="https://www.geeksforgeeks.org/ml-feature-scaling-part-2/">Feature Scaling – Part 2</a></li><li><a href="https://www.geeksforgeeks.org/ml-label-encoding-of-datasets-in-python/">Python | Label Encoding of datasets</a></li><li><a href="https://www.geeksforgeeks.org/ml-one-hot-encoding-of-datasets-in-python/">Python | One Hot Encoding of datasets</a></li><li><a href="https://www.geeksforgeeks.org/ml-handling-imbalanced-data-with-smote-and-near-miss-algorithm-in-python/">Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python</a></li><li><a href="https://www.geeksforgeeks.org/ml-dummy-variable-trap-in-regression-models/?ref=rp">Dummy variable trap in Regression Models</a></li></ol></div><h2><a></a>Supervised learning :</h2><div><ol><li><a href="https://www.geeksforgeeks.org/getting-started-with-classification/">Getting started with Classification</a></li><li><a href="https://www.geeksforgeeks.org/basic-concept-classification-data-mining/">Basic Concept of Classification</a></li><li><a href="https://www.geeksforgeeks.org/types-of-regression-techniques/">Types of Regression Techniques</a></li><li><a href="https://www.geeksforgeeks.org/ml-classification-vs-regression/">Classification vs Regression</a></li><li><a href="https://www.geeksforgeeks.org/ml-types-learning-supervised-learning/">ML | Types of Learning – Supervised Learning</a></li><li><a href="https://www.geeksforgeeks.org/multiclass-classification-using-scikit-learn/">Multiclass classification using scikit-learn</a></li><li><ul><strong>Gradient Descent :</strong><li><a href="https://www.geeksforgeeks.org/gradient-descent-algorithm-and-its-variants/">Gradient Descent algorithm and its variants</a></li><li><a href="https://www.geeksforgeeks.org/ml-stochastic-gradient-descent-sgd/">Stochastic Gradient Descent (SGD)</a></li><li><a href="https://www.geeksforgeeks.org/ml-mini-batch-gradient-descent-with-python/">Mini-Batch Gradient Descent with Python</a></li><li><a href="https://www.geeksforgeeks.org/optimization-techniques-for-gradient-descent/">Optimization techniques for Gradient Descent</a></li><li><a href="https://www.geeksforgeeks.org/ml-momentum-based-gradient-optimizer-introduction/">Introduction to Momentum-based Gradient Optimizer</a></li></ul></li><li><ul><strong>Linear Regression :</strong><li><a href="https://www.geeksforgeeks.org/ml-linear-regression/">Introduction to Linear Regression</a></li><li><a href="https://www.geeksforgeeks.org/gradient-descent-in-linear-regression/">Gradient Descent in Linear Regression</a></li><li><a href="https://www.geeksforgeeks.org/mathematical-explanation-for-linear-regression-working/">Mathematical explanation for Linear Regression working</a></li><li><a href="https://www.geeksforgeeks.org/ml-normal-equation-in-linear-regression/">Normal Equation in Linear Regression</a></li><li><a href="https://www.geeksforgeeks.org/linear-regression-python-implementation/">Linear Regression (Python Implementation)</a></li><li><a href="https://www.geeksforgeeks.org/simple-linear-regression-using-r/">Simple Linear-Regression using R</a></li><li><a href="https://www.geeksforgeeks.org/univariate-linear-regression-in-python/">Univariate Linear Regression in Python</a></li><li><a href="https://www.geeksforgeeks.org/ml-multiple-linear-regression-using-python/">Multiple Linear Regression using Python</a></li><li><a href="https://www.geeksforgeeks.org/multiple-linear-regression-using-r/">Multiple Linear Regression using R</a></li><li><a href="https://www.geeksforgeeks.org/ml-locally-weighted-linear-regression/">Locally weighted Linear Regression</a></li><li><a href="https://www.geeksforgeeks.org/generalized-linear-models/">Generalized Linear Models</a></li><li><a href="https://www.geeksforgeeks.org/python-linear-regression-using-sklearn/">Python | Linear Regression using sklearn</a></li><li><a href="https://www.geeksforgeeks.org/linear-regression-using-tensorflow/">Linear Regression Using Tensorflow</a></li><li><a href="https://www.geeksforgeeks.org/a-practical-approach-to-simple-linear-regression-using-r/">A Practical approach to Simple Linear Regression using R</a></li><li><a href="https://www.geeksforgeeks.org/linear-regression-using-pytorch/">Linear Regression using PyTorch</a></li><li><a href="https://www.geeksforgeeks.org/pyspark-linear-regression-using-apache-mllib/">Pyspark | Linear regression using Apache MLlib<br /></a></li><li><a href="https://www.geeksforgeeks.org/ml-boston-housing-kaggle-challenge-with-linear-regression/">ML | Boston Housing Kaggle Challenge with Linear Regression</a></li></ul></li><li><a href="https://www.geeksforgeeks.org/python-implementation-of-polynomial-regression/">Python | Implementation of <strong>Polynomial Regression</strong></a></li><li><a href="https://www.geeksforgeeks.org/softmax-regression-using-tensorflow/"><strong>Softmax Regression</strong> using TensorFlow</a></li><li><ul><strong>Logistic Regression :</strong><li><a href="https://www.geeksforgeeks.org/understanding-logistic-regression/">Understanding Logistic Regression</a></li><li><a href="https://www.geeksforgeeks.org/ml-why-logistic-regression-in-classification/">Why Logistic Regression in Classification ?</a></li><li><a href="https://www.geeksforgeeks.org/ml-logistic-regression-using-python/">Logistic Regression using Python</a></li><li><a href="https://www.geeksforgeeks.org/ml-cost-function-in-logistic-regression/">Cost function in Logistic Regression</a></li><li><a href="https://www.geeksforgeeks.org/ml-logistic-regression-using-tensorflow/">Logistic Regression using Tensorflow</a></li></ul></li><li><a href="https://www.geeksforgeeks.org/naive-bayes-classifiers/"><strong>Naive Bayes</strong> Classifiers</a></li><li><ul><strong>Support Vector:</strong><li><a href="https://www.geeksforgeeks.org/classifying-data-using-support-vector-machinessvms-in-python/">Support Vector Machines(SVMs) in Python</a></li><li><a href="https://www.geeksforgeeks.org/svm-hyperparameter-tuning-using-gridsearchcv-ml/">SVM Hyperparameter Tuning using GridSearchCV</a></li><li><a href="https://www.geeksforgeeks.org/classifying-data-using-support-vector-machinessvms-in-r/">Support Vector Machines(SVMs) in R</a></li><li><a href="https://www.geeksforgeeks.org/ml-using-svm-to-perform-classification-on-a-non-linear-dataset/">Using SVM to perform classification on a non-linear dataset</a></li></ul></li><li><ul><strong>Decision Tree:</strong><li><a href="https://www.geeksforgeeks.org/decision-tree/">Decision Tree</a></li><li><a href="https://www.geeksforgeeks.org/python-decision-tree-regression-using-sklearn/">Decision Tree Regression using sklearn</a></li><li><a href="https://www.geeksforgeeks.org/decision-tree-introduction-example/">Decision Tree Introduction with example</a></li><li><a href="https://www.geeksforgeeks.org/decision-tree-implementation-python/">Decision tree implementation using Python</a></li><li><a href="https://www.geeksforgeeks.org/decision-tree-in-software-engineering/">Decision Tree in Software Engineering</a></li></ul></li><li><ul><strong>Random Forest:</strong><li><a href="https://www.geeksforgeeks.org/random-forest-regression-in-python/">Random Forest Regression in Python</a></li><li><a href="https://www.geeksforgeeks.org/ensemble-classifier-data-mining/">Ensemble Classifier</a></li><li><a href="https://www.geeksforgeeks.org/ml-voting-classifier-using-sklearn/">Voting Classifier using Sklearn<br /></a></li><li><a href="https://www.geeksforgeeks.org/ml-bagging-classifier/">Bagging classifier</a></li></ul></li></ol></div><h2><a></a>Unsupervised learning :</h2><div><ol><li><a href="https://www.geeksforgeeks.org/ml-types-learning-part-2/">ML | Types of Learning – Unsupervised Learning</a></li><li><a href="https://www.geeksforgeeks.org/supervised-unsupervised-learning/">Supervised and Unsupervised learning</a></li><li><a href="https://www.geeksforgeeks.org/clustering-in-machine-learning/">Clustering in Machine Learning</a></li><li><a href="https://www.geeksforgeeks.org/different-types-clustering-algorithm/">Different Types of Clustering Algorithm</a></li><li><a href="https://www.geeksforgeeks.org/k-means-clustering-introduction/">K means Clustering – Introduction</a></li><li><a href="https://www.geeksforgeeks.org/elbow-method-for-optimal-value-of-k-in-kmeans/">Elbow Method for optimal value of k in KMeans</a></li><li><a href="https://www.geeksforgeeks.org/ml-random-intialization-trap-in-k-means/?ref=rp">Random Initialization Trap in K-Means</a></li><li><a href="https://www.geeksforgeeks.org/ml-k-means-algorithm/">ML | K-means++ Algorithm</a></li><li><a href="https://www.geeksforgeeks.org/analysis-of-test-data-using-k-means-clustering-in-python/">Analysis of test data using K-Means Clustering in Python</a></li><li><a href="https://www.geeksforgeeks.org/ml-mini-batch-k-means-clustering-algorithm/">Mini Batch K-means clustering algorithm</a></li><li><a href="https://www.geeksforgeeks.org/ml-mean-shift-clustering/">Mean-Shift Clustering</a></li><li><a href="https://www.geeksforgeeks.org/dbscan-clustering-in-ml-density-based-clustering/">DBSCAN – Density based clustering</a></li><li><a href="https://www.geeksforgeeks.org/implementing-dbscan-algorithm-using-sklearn/">Implementing DBSCAN algorithm using Sklearn</a></li><li><a href="https://www.geeksforgeeks.org/ml-fuzzy-clustering/">Fuzzy Clustering</a></li><li><a href="https://www.geeksforgeeks.org/ml-spectral-clustering/">Spectral Clustering</a></li><li><a href="https://www.geeksforgeeks.org/ml-optics-clustering-explanation/">OPTICS Clustering</a></li><li><a href="https://www.geeksforgeeks.org/ml-optics-clustering-implementing-using-sklearn/">OPTICS Clustering Implementing using Sklearn</a></li><li><a href="https://www.geeksforgeeks.org/ml-hierarchical-clustering-agglomerative-and-divisive-clustering/">Hierarchical clustering (Agglomerative and Divisive clustering)</a></li><li><a href="https://www.geeksforgeeks.org/implementing-agglomerative-clustering-using-sklearn/">Implementing Agglomerative Clustering using Sklearn</a></li><li><a href="https://www.geeksforgeeks.org/gaussian-mixture-model/">Gaussian Mixture Model</a></li></ol></div><h2><a></a>Reinforcement Learning:</h2><div><ol><li><a href="https://www.geeksforgeeks.org/what-is-reinforcement-learning/">Reinforcement learning</a></li><li><a href="https://www.geeksforgeeks.org/ml-reinforcement-learning-algorithm-python-implementation-using-q-learning/">Reinforcement Learning Algorithm : Python Implementation using Q-learning</a></li><li><a href="https://www.geeksforgeeks.org/introduction-to-thompson-sampling-reinforcement-learning/">Introduction to Thompson Sampling</a></li><li><a href="https://www.geeksforgeeks.org/genetic-algorithm-for-reinforcement-learning-python-implementation/">Genetic Algorithm for Reinforcement Learning</a></li><li><a href="https://www.geeksforgeeks.org/sarsa-reinforcement-learning/">SARSA Reinforcement Learning</a></li><li><a href="https://www.geeksforgeeks.org/q-learning-in-python/">Q-Learning in Python</a></li></ol></div><h2><a></a>Dimensionality Reduction :</h2><div><ol><li><a href="https://www.geeksforgeeks.org/dimensionality-reduction/">Introduction to Dimensionality Reduction</a></li><li><a href="https://www.geeksforgeeks.org/ml-introduction-to-kernel-pca/">Introduction to Kernel PCA</a></li><li><a href="https://www.geeksforgeeks.org/ml-principal-component-analysispca/">Principal Component Analysis(PCA)</a></li><li><a href="https://www.geeksforgeeks.org/principal-component-analysis-with-python/">Principal Component Analysis with Python</a></li><li><a href="https://www.geeksforgeeks.org/eigenvector-computation-and-low-rank-approximations/">Low-Rank Approximations</a></li><li><a href="https://www.geeksforgeeks.org/ml-linear-discriminant-analysis/">Overview of Linear Discriminant Analysis </a>(LDA)</li><li>Mathematical Explanation of Linear Discriminant Analysis (LDA)</li><li><a href="https://www.geeksforgeeks.org/gaussian-discriminant-analysis/">Generalized Discriminant Analysis (GDA)</a></li><li><a href="https://www.geeksforgeeks.org/ml-independent-component-analysis/">Independent Component Analysis</a></li><li><a href="https://www.geeksforgeeks.org/feature-mapping/">Feature Mapping</a></li><li><a href="https://www.geeksforgeeks.org/ml-extra-tree-classifier-for-feature-selection/">Extra Tree Classifier for Feature Selection</a></li><li><a href="https://www.geeksforgeeks.org/chi-square-test-for-feature-selection-mathematical-explanation/">Chi-Square Test for Feature Selection – Mathematical Explanation</a></li><li><a href="https://www.geeksforgeeks.org/ml-t-distributed-stochastic-neighbor-embedding-t-sne-algorithm/">ML | T-distributed Stochastic Neighbor Embedding (t-SNE) Algorithm</a></li><li><a href="https://www.geeksforgeeks.org/python-how-and-where-to-apply-feature-scaling/">Python | How and where to apply Feature Scaling?</a></li><li><a href="https://www.geeksforgeeks.org/parameters-feature-selection/">Parameters for Feature Selection</a></li><li><a href="https://www.geeksforgeeks.org/underfitting-and-overfitting-in-machine-learning/">Underfitting and Overfitting in Machine Learning</a></li></ol></div><h2><a></a>Natural Language Processing :</h2><div><ol><li><a href="https://www.geeksforgeeks.org/introduction-to-natural-language-processing/">Introduction to Natural Language Processing</a></li><li><a href="https://www.geeksforgeeks.org/text-preprocessing-in-python-set-1/">Text Preprocessing in Python | Set – 1</a></li><li><a href="https://www.geeksforgeeks.org/text-preprocessing-in-python-set-2/">Text Preprocessing in Python | Set 2</a></li><li><a href="https://www.geeksforgeeks.org/removing-stop-words-nltk-python/">Removing stop words with NLTK in Python</a></li><li><a href="https://www.geeksforgeeks.org/tokenize-text-using-nltk-python/">Tokenize text using NLTK in python</a></li><li><a href="https://www.geeksforgeeks.org/nlp-how-tokenizing-text-sentence-words-works/">How tokenizing text, sentence, words works</a></li><li><a href="https://www.geeksforgeeks.org/introduction-to-stemming/">Introduction to Stemming</a></li><li><a href="https://www.geeksforgeeks.org/python-stemming-words-with-nltk/">Stemming words with NLTK</a></li><li><a href="https://www.geeksforgeeks.org/python-lemmatization-with-nltk/">Lemmatization with NLTK</a></li><li><a href="https://www.geeksforgeeks.org/python-lemmatization-with-textblob/">Lemmatization with TextBlob</a></li><li><a href="https://www.geeksforgeeks.org/get-synonymsantonyms-nltk-wordnet-python/">How to get synonyms/antonyms from NLTK WordNet in Python?</a></li></ol></div><h2><a></a>Neural Networks :</h2><div><ol><li><a href="https://www.geeksforgeeks.org/introduction-to-artificial-neutral-networks/">Introduction to Artificial Neutral Networks | Set 1</a></li><li><a href="https://www.geeksforgeeks.org/introduction-artificial-neural-network-set-2/">Introduction to Artificial Neural Network | Set 2</a></li><li><a href="https://www.geeksforgeeks.org/introduction-ann-artificial-neural-networks-set-3-hybrid-systems/">Introduction to ANN (Artificial Neural Networks) | Set 3 (Hybrid Systems)</a></li><li><a href="https://www.geeksforgeeks.org/introduction-to-ann-set-4-network-architectures/">Introduction to ANN | Set 4 (Network Architectures)</a></li><li><a href="https://www.geeksforgeeks.org/activation-functions-neural-networks/">Activation functions</a></li><li><a href="https://www.geeksforgeeks.org/implementing-ann-training-process-in-python/">Implementing Artificial Neural Network training process in Python</a></li><li><a href="https://www.geeksforgeeks.org/single-neuron-neural-network-python/">A single neuron neural network in Python</a></li><li><ul><strong>Convolutional Neural Networks</strong><li><a href="https://www.geeksforgeeks.org/introduction-convolution-neural-network/">Introduction to Convolution Neural Network</a></li><li><a href="https://www.geeksforgeeks.org/cnn-introduction-to-pooling-layer/">Introduction to Pooling Layer</a></li><li><a href="https://www.geeksforgeeks.org/cnn-introduction-to-padding/">Introduction to Padding</a></li><li><a href="https://www.geeksforgeeks.org/types-of-padding-in-convolution-layer/">Types of padding in convolution layer</a></li><li><a href="https://www.geeksforgeeks.org/applying-convolutional-neural-network-on-mnist-dataset/">Applying Convolutional Neural Network on mnist dataset</a></li></ul></li><li><ul><strong>Recurrent Neural Networks</strong><li><a href="https://www.geeksforgeeks.org/introduction-to-recurrent-neural-network/">Introduction to Recurrent Neural Network</a></li><li><a href="https://www.geeksforgeeks.org/recurrent-neural-networks-explanation/">Recurrent Neural Networks Explanation</a></li><li><a href="https://www.geeksforgeeks.org/seq2seq-model-in-machine-learning/">seq2seq model</a></li><li><a href="https://www.geeksforgeeks.org/deep-learning-introduction-to-long-short-term-memory/">Introduction to Long Short Term Memory</a></li><li><a href="https://www.geeksforgeeks.org/long-short-term-memory-networks-explanation/">Long Short Term Memory Networks Explanation</a></li><li><a href="https://www.geeksforgeeks.org/gated-recurrent-unit-networks/">Gated Recurrent Unit Networks(GAN)</a></li><li><a href="https://www.geeksforgeeks.org/ml-text-generation-using-gated-recurrent-unit-networks/">Text Generation using Gated Recurrent Unit Networks</a></li></ul></li><li><ul><strong>GANs – Generative Adversarial Network</strong><li><a href="https://www.geeksforgeeks.org/generative-adversarial-network-gan/">Introduction to Generative Adversarial Network</a></li><li><a href="https://www.geeksforgeeks.org/generative-adversarial-networks-gans-an-introduction/">Generative Adversarial Networks (GANs)</a></li><li><a href="https://www.geeksforgeeks.org/use-cases-of-generative-adversarial-networks/">Use Cases of Generative Adversarial Networks</a></li><li><a href="https://www.geeksforgeeks.org/building-a-generative-adversarial-network-using-keras/">Building a Generative Adversarial Network using Keras</a></li><li><a href="https://www.geeksforgeeks.org/modal-collapse-in-gans/">Modal Collapse in GANs</a></li></ul></li><li><a href="https://www.geeksforgeeks.org/deep-q-learning/">Introduction to Deep Q-Learning</a></li><li><a href="https://www.geeksforgeeks.org/implementing-deep-q-learning-using-tensorflow/">Implementing Deep Q-Learning using Tensorflow</a></li></ol></div><h2><a></a>ML – Deployment :</h2><div><ol><li><a href="https://www.geeksforgeeks.org/deploy-your-machine-learning-web-app-streamlit-on-heroku/?ref=gcse">Deploy your Machine Learning web app (Streamlit) on Heroku</a></li><li><a href="https://www.geeksforgeeks.org/deploy-a-machine-learning-model-using-streamlit-library/?ref=rp">Deploy a Machine Learning Model using Streamlit Library</a></li><li><a href="https://www.geeksforgeeks.org/deploy-machine-learning-model-using-flask/?ref=rp">Deploy Machine Learning Model using Flask</a></li><li><a href="https://www.geeksforgeeks.org/python-create-uis-for-prototyping-machine-learning-model-with-gradio/?ref=rp">Python – Create UIs for prototyping Machine Learning model with Gradio</a></li><li><a href="https://www.geeksforgeeks.org/how-to-prepare-data-before-deploying-a-machine-learning-model/?ref=rp">How to Prepare Data Before Deploying a Machine Learning Model?</a></li><li><a href="https://www.geeksforgeeks.org/deploying-ml-models-as-api-using-fastapi/?ref=rp">Deploying ML Models as API using FastAPI<br /></a></li><li><a href="https://www.geeksforgeeks.org/deploying-scrapy-spider-scrapinghub/?ref=rp">Deploying Scrapy spider on ScrapingHub</a></li></ol></div><h2><a></a>ML – Applications :</h2><div><ol><li><a href="https://www.geeksforgeeks.org/ml-rainfall-prediction-using-linear-regression/">Rainfall prediction using Linear regression</a></li><li><a href="https://www.geeksforgeeks.org/identifying-handwritten-digits-using-logistic-regression-pytorch/">Identifying handwritten digits using Logistic Regression in PyTorch</a></li><li><a href="https://www.geeksforgeeks.org/ml-kaggle-breast-cancer-wisconsin-diagnosis-using-logistic-regression/">Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression</a></li><li><a href="https://www.geeksforgeeks.org/python-implementation-of-movie-recommender-system/">Python | Implementation of Movie Recommender System</a></li><li><a href="https://www.geeksforgeeks.org/train-a-support-vector-machine-to-recognize-facial-features-in-c/">Support Vector Machine to recognize facial features in C++</a></li><li><a href="https://www.geeksforgeeks.org/decision-trees-fake-coin-puzzle/">Decision Trees – Fake (Counterfeit) Coin Puzzle (12 Coin Puzzle)</a></li><li><a href="https://www.geeksforgeeks.org/ml-credit-card-fraud-detection/">Credit Card Fraud Detection</a></li><li><a href="https://www.geeksforgeeks.org/python-nlp-analysis-of-restaurant-reviews/">NLP analysis of Restaurant reviews</a></li><li><a href="https://www.geeksforgeeks.org/applying-multinomial-naive-bayes-to-nlp-problems/">Applying Multinomial Naive Bayes to NLP Problems</a></li><li><a href="https://www.geeksforgeeks.org/image-compression-using-k-means-clustering/">Image compression using K-means clustering</a></li><li><a href="https://www.geeksforgeeks.org/avengers-endgame-and-deep-learning/">Deep learning | Image Caption Generation using the Avengers EndGames Characters</a></li><li><a href="https://www.geeksforgeeks.org/how-does-google-use-machine-learning/">How Does Google Use Machine Learning?</a></li><li><a href="https://www.geeksforgeeks.org/how-does-nasa-use-machine-learning/">How Does NASA Use Machine Learning?</a></li><li><a href="https://www.geeksforgeeks.org/5-mind-blowing-ways-facebook-uses-machine-learning/">5 Mind-Blowing Ways Facebook Uses Machine Learning<br /></a></li><li><a href="https://www.geeksforgeeks.org/targeted-advertising-using-machine-learning/">Targeted Advertising using Machine Learning</a></li><li><a href="https://www.geeksforgeeks.org/how-machine-learning-is-used-by-famous-companies/">How Machine Learning Is Used by Famous Companies?</a></li></ol></div><h2><a></a>Misc :</h2><div><ol><li><a href="https://www.geeksforgeeks.org/pattern-recognition-introduction/">Pattern Recognition | Introduction</a></li><li><a href="https://www.geeksforgeeks.org/calculate-efficiency-binary-classifier/">Calculate Efficiency Of Binary Classifier</a></li><li><a href="https://www.geeksforgeeks.org/ml-logistic-regression-v-s-decision-tree-classification/">Logistic Regression v/s Decision Tree Classification</a></li><li><a href="https://www.geeksforgeeks.org/r-vs-python-datascience/">R vs Python in Datascience</a></li><li><a href="https://www.geeksforgeeks.org/explanation-of-fundamental-functions-involved-in-a3c-algorithm/">Explanation of Fundamental Functions involved in A3C algorithm</a></li><li><a href="https://www.geeksforgeeks.org/differential-privacy-and-deep-learning/">Differential Privacy and Deep Learning</a></li><li><a href="https://www.geeksforgeeks.org/artificial-intelligence-vs-machine-learning-vs-deep-learning/">Artificial intelligence vs Machine Learning vs Deep Learning</a></li><li><a href="https://www.geeksforgeeks.org/introduction-to-multi-task-learningmtl-for-deep-learning/">Introduction to Multi-Task Learning(MTL) for Deep Learning</a></li><li><a href="https://www.geeksforgeeks.org/top-10-algorithms-every-machine-learning-engineer-should-know/">Top 10 Algorithms every Machine Learning Engineer should know</a></li><li><a href="https://www.geeksforgeeks.org/azure-virtual-machine-for-machine-learning/">Azure Virtual Machine for Machine Learning</a></li><li><a href="https://www.geeksforgeeks.org/30-minutes-to-machine-learning/">30 minutes to machine learning</a></li><li><a href="https://www.geeksforgeeks.org/what-is-automl-in-machine-learning/">What is AutoML in Machine Learning?</a></li><li><a href="https://www.geeksforgeeks.org/confusion-matrix-machine-learning/">Confusion Matrix in Machine Learning</a></li></ol></div><h2>Prerequisites to learn machine learning</h2><ul><li>Knowledge of Linear equations, graphs of functions, statistics, Linear Algebra, Probability, Calculus etc.</li><li>Any programming language knowledge like Python, C++, R are recommended.</li><h3>Q.1 What is Machine learning and how is it different from Deep learning ?</h3><p><strong>Answer</strong>:</p><blockquote><p>Machine learning develop programs that can access data and learn from it. Deep learning is the sub domain of the machine learning. Deep learning supports automatic extraction of features from the raw data.</p></blockquote><h3>Q.2. What are the different type of machine learning algorithms ?</h3><p><strong>Answer</strong>:</p><blockquote><ul><li>Supervised algorithms: These are the algorithms which learn from the labelled data, e.g. images labelled with dog face or not. Algorithm depends on supervised or labelled data. e.g. regression, object detection, segmentation.</li><li>Non-Supervised algorithms: These are the algorithms which learn from the non labelled data, e.g. bunch of images given to make a similar set of images. e.g. clustering, dimensionality reduction etc.</li><li>Semi-Supervised algorithms: Algorithms that uses both supervised or non-supervised data. Majority portion of data use for these algorithms are not supervised data. e.g. anamoly detection.</li></ul></blockquote><h3>Q.3. Why we use machine learning ?</h3><p><strong>Answer</strong>:</p><blockquote><p>Machine learning is used to make decisions based on data. By modelling the algorithms on the bases of historical data, Algorithms find the patterns and relationships that are difficult for humans to detect. These patterns are now further use for the future references to predict solution of unseen problems.</p></blockquote><h3>Q.4. What is the difference between Artificial Intelligence and Machine learning ?</h3><p><strong>Answer</strong>:</p><blockquote><table><thead><tr><th>Artificial Intelligence</th><th>Machine Learning</th></tr></thead><tbody><tr><td>Develop an intelligent system that perform variety of complex jobs.</td><td>Construct machines that can only accomplish the jobs for which they have trained.</td></tr><tr><td>It works as a program that does smart work.</td><td>The tasks systems machine takes data and learns from data.</td></tr><tr><td>AI has broad variety of applications.</td><td>ML allows systems to learn new things from data.</td></tr><tr><td>AI leads wisdom.</td><td>ML leads to knowledge.</td></tr></tbody></table></blockquote><br /></ul></div></article></div>||||I|||| Skip to content
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 Machine Learning Tutorial
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 Machine Learning tutorial covers basic and advanced concepts, specially designed to cater to both students and experienced working professionals.

 This machine learning tutorial helps you gain a solid introduction to the fundamentals of machine learning and explore a wide range of techniques, including supervised, unsupervised, and reinforcement learning.

 What is Machine Learning?

 Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.

 Recent Articles on Machine Learning

 * Introduction
 * Data and it’s Processing
 * Supervised Learning
 * Unsupervised Learning
 * Reinforcement Learning
 * Dimensionality Reduction
 * Natural Language Processing
 * Neural Networks
 * ML – Deployment
 * ML – Applications
 * Miscellaneous

 Features of Machine learning

 * Machine learning is data driven technology. Large amount of data generated by organizations on daily bases. So, by notable relationships in data, organizations makes better decisions.
 * Machine can learn itself from past data and automatically improve.
 * From the given dataset it detects various patterns on data.
 * For the big organizations branding is important and it will become more easy to target relatable customer base.
 * It is similar to data mining because it is also deals with the huge amount of data.

 Introduction :

 1. Getting Started with Machine Learning
 2. An Introduction to Machine Learning
 3. What is Machine Learning ?
 4. Introduction to Data in Machine Learning
 5. Demystifying Machine Learning
 6. ML – Applications
 7. Best Python libraries for Machine Learning
 8. Artificial Intelligence | An Introduction
 9. Machine Learning and Artificial Intelligence
 10. Difference between Machine learning and Artificial Intelligence
 11. Agents in Artificial Intelligence
 12. 10 Basic Machine Learning Interview Questions

 Data and It’s Processing:

 1. Introduction to Data in Machine Learning
 2. Understanding Data Processing
 3. Python | Create Test DataSets using Sklearn
 4. Python | Generate test datasets for Machine learning
 5. Python | Data Preprocessing in Python
 6. Data Cleaning
 7. Feature Scaling – Part 1
 8. Feature Scaling – Part 2
 9. Python | Label Encoding of datasets
 10. Python | One Hot Encoding of datasets
 11. Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python
 12. Dummy variable trap in Regression Models

 Supervised learning :

 1. Getting started with Classification
 2. Basic Concept of Classification
 3. Types of Regression Techniques
 4. Classification vs Regression
 5. ML | Types of Learning – Supervised Learning
 6. Multiclass classification using scikit-learn
 7. Gradient Descent :
 + Gradient Descent algorithm and its variants
 + Stochastic Gradient Descent (SGD)
 + Mini-Batch Gradient Descent with Python
 + Optimization techniques for Gradient Descent
 + Introduction to Momentum-based Gradient Optimizer
 8. Linear Regression :
 + Introduction to Linear Regression
 + Gradient Descent in Linear Regression
 + Mathematical explanation for Linear Regression working
 + Normal Equation in Linear Regression
 + Linear Regression (Python Implementation)
 + Simple Linear-Regression using R
 + Univariate Linear Regression in Python
 + Multiple Linear Regression using Python
 + Multiple Linear Regression using R
 + Locally weighted Linear Regression
 + Generalized Linear Models
 + Python | Linear Regression using sklearn
 + Linear Regression Using Tensorflow
 + A Practical approach to Simple Linear Regression using R
 + Linear Regression using PyTorch
 + Pyspark | Linear regression using Apache MLlib
 + ML | Boston Housing Kaggle Challenge with Linear Regression
 9. Python | Implementation of Polynomial Regression
 10. Softmax Regression using TensorFlow
 11. Logistic Regression :
 + Understanding Logistic Regression
 + Why Logistic Regression in Classification ?
 + Logistic Regression using Python
 + Cost function in Logistic Regression
 + Logistic Regression using Tensorflow
 12. Naive Bayes Classifiers
 13. Support Vector:
 + Support Vector Machines(SVMs) in Python
 + SVM Hyperparameter Tuning using GridSearchCV
 + Support Vector Machines(SVMs) in R
 + Using SVM to perform classification on a non-linear dataset
 14. Decision Tree:
 + Decision Tree
 + Decision Tree Regression using sklearn
 + Decision Tree Introduction with example
 + Decision tree implementation using Python
 + Decision Tree in Software Engineering
 15. Random Forest:
 + Random Forest Regression in Python
 + Ensemble Classifier
 + Voting Classifier using Sklearn
 + Bagging classifier

 Unsupervised learning :

 1. ML | Types of Learning – Unsupervised Learning
 2. Supervised and Unsupervised learning
 3. Clustering in Machine Learning
 4. Different Types of Clustering Algorithm
 5. K means Clustering – Introduction
 6. Elbow Method for optimal value of k in KMeans
 7. Random Initialization Trap in K-Means
 8. ML | K-means++ Algorithm
 9. Analysis of test data using K-Means Clustering in Python
 10. Mini Batch K-means clustering algorithm
 11. Mean-Shift Clustering
 12. DBSCAN – Density based clustering
 13. Implementing DBSCAN algorithm using Sklearn
 14. Fuzzy Clustering
 15. Spectral Clustering
 16. OPTICS Clustering
 17. OPTICS Clustering Implementing using Sklearn
 18. Hierarchical clustering (Agglomerative and Divisive clustering)
 19. Implementing Agglomerative Clustering using Sklearn
 20. Gaussian Mixture Model

 Reinforcement Learning:

 1. Reinforcement learning
 2. Reinforcement Learning Algorithm : Python Implementation using Q-learning
 3. Introduction to Thompson Sampling
 4. Genetic Algorithm for Reinforcement Learning
 5. SARSA Reinforcement Learning
 6. Q-Learning in Python

 Dimensionality Reduction :

 1. Introduction to Dimensionality Reduction
 2. Introduction to Kernel PCA
 3. Principal Component Analysis(PCA)
 4. Principal Component Analysis with Python
 5. Low-Rank Approximations
 6. Overview of Linear Discriminant Analysis (LDA)
 7. Mathematical Explanation of Linear Discriminant Analysis (LDA)
 8. Generalized Discriminant Analysis (GDA)
 9. Independent Component Analysis
 10. Feature Mapping
 11. Extra Tree Classifier for Feature Selection
 12. Chi-Square Test for Feature Selection – Mathematical Explanation
 13. ML | T-distributed Stochastic Neighbor Embedding (t-SNE) Algorithm
 14. Python | How and where to apply Feature Scaling?
 15. Parameters for Feature Selection
 16. Underfitting and Overfitting in Machine Learning

 Natural Language Processing :

 1. Introduction to Natural Language Processing
 2. Text Preprocessing in Python | Set – 1
 3. Text Preprocessing in Python | Set 2
 4. Removing stop words with NLTK in Python
 5. Tokenize text using NLTK in python
 6. How tokenizing text, sentence, words works
 7. Introduction to Stemming
 8. Stemming words with NLTK
 9. Lemmatization with NLTK
 10. Lemmatization with TextBlob
 11. How to get synonyms/antonyms from NLTK WordNet in Python?

 Neural Networks :

 1. Introduction to Artificial Neutral Networks | Set 1
 2. Introduction to Artificial Neural Network | Set 2
 3. Introduction to ANN (Artificial Neural Networks) | Set 3 (Hybrid Systems)
 4. Introduction to ANN | Set 4 (Network Architectures)
 5. Activation functions
 6. Implementing Artificial Neural Network training process in Python
 7. A single neuron neural network in Python
 8. Convolutional Neural Networks
 + Introduction to Convolution Neural Network
 + Introduction to Pooling Layer
 + Introduction to Padding
 + Types of padding in convolution layer
 + Applying Convolutional Neural Network on mnist dataset
 9. Recurrent Neural Networks
 + Introduction to Recurrent Neural Network
 + Recurrent Neural Networks Explanation
 + seq2seq model
 + Introduction to Long Short Term Memory
 + Long Short Term Memory Networks Explanation
 + Gated Recurrent Unit Networks(GAN)
 + Text Generation using Gated Recurrent Unit Networks
 10. GANs – Generative Adversarial Network
 + Introduction to Generative Adversarial Network
 + Generative Adversarial Networks (GANs)
 + Use Cases of Generative Adversarial Networks
 + Building a Generative Adversarial Network using Keras
 + Modal Collapse in GANs
 11. Introduction to Deep Q-Learning
 12. Implementing Deep Q-Learning using Tensorflow

 ML – Deployment :

 1. Deploy your Machine Learning web app (Streamlit) on Heroku
 2. Deploy a Machine Learning Model using Streamlit Library
 3. Deploy Machine Learning Model using Flask
 4. Python – Create UIs for prototyping Machine Learning model with Gradio
 5. How to Prepare Data Before Deploying a Machine Learning Model?
 6. Deploying ML Models as API using FastAPI
 7. Deploying Scrapy spider on ScrapingHub

 ML – Applications :

 1. Rainfall prediction using Linear regression
 2. Identifying handwritten digits using Logistic Regression in PyTorch
 3. Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression
 4. Python | Implementation of Movie Recommender System
 5. Support Vector Machine to recognize facial features in C++
 6. Decision Trees – Fake (Counterfeit) Coin Puzzle (12 Coin Puzzle)
 7. Credit Card Fraud Detection
 8. NLP analysis of Restaurant reviews
 9. Applying Multinomial Naive Bayes to NLP Problems
 10. Image compression using K-means clustering
 11. Deep learning | Image Caption Generation using the Avengers EndGames Characters
 12. How Does Google Use Machine Learning?
 13. How Does NASA Use Machine Learning?
 14. 5 Mind-Blowing Ways Facebook Uses Machine Learning
 15. Targeted Advertising using Machine Learning
 16. How Machine Learning Is Used by Famous Companies?

 Misc :

 1. Pattern Recognition | Introduction
 2. Calculate Efficiency Of Binary Classifier
 3. Logistic Regression v/s Decision Tree Classification
 4. R vs Python in Datascience
 5. Explanation of Fundamental Functions involved in A3C algorithm
 6. Differential Privacy and Deep Learning
 7. Artificial intelligence vs Machine Learning vs Deep Learning
 8. Introduction to Multi-Task Learning(MTL) for Deep Learning
 9. Top 10 Algorithms every Machine Learning Engineer should know
 10. Azure Virtual Machine for Machine Learning
 11. 30 minutes to machine learning
 12. What is AutoML in Machine Learning?
 13. Confusion Matrix in Machine Learning

 Prerequisites to learn machine learning

 * Knowledge of Linear equations, graphs of functions, statistics, Linear Algebra, Probability, Calculus etc.
 * Any programming language knowledge like Python, C++, R are recommended.

 FAQs on Machine Learning Tutorial

 Q.1 What is Machine learning and how is it different from Deep learning ?

 Answer:

 Machine learning develop programs that can access data and learn from it. Deep learning is the sub domain of the machine learning. Deep learning supports automatic extraction of features from the raw data.

 Q.2. What are the different type of machine learning algorithms ?

 Answer:

 + Supervised algorithms: These are the algorithms which learn from the labelled data, e.g. images labelled with dog face or not. Algorithm depends on supervised or labelled data. e.g. regression, object detection, segmentation.
 + Non-Supervised algorithms: These are the algorithms which learn from the non labelled data, e.g. bunch of images given to make a similar set of images. e.g. clustering, dimensionality reduction etc.
 + Semi-Supervised algorithms: Algorithms that uses both supervised or non-supervised data. Majority portion of data use for these algorithms are not supervised data. e.g. anamoly detection.

 Q.3. Why we use machine learning ?

 Answer:

 Machine learning is used to make decisions based on data. By modelling the algorithms on the bases of historical data, Algorithms find the patterns and relationships that are difficult for humans to detect. These patterns are now further use for the future references to predict solution of unseen problems.

 Q.4. What is the difference between Artificial Intelligence and Machine learning ?

 Answer:

 Artificial Intelligence Machine Learning  
 Develop an intelligent system that perform variety of complex jobs. Construct machines that can only accomplish the jobs for which they have trained.
 It works as a program that does smart work. The tasks systems machine takes data and learns from data. 
 AI has broad variety of applications. ML allows systems to learn new things from data. 
 AI leads wisdom. ML leads to knowledge. 
 

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Machine Learning tutorial covers basic and advanced concepts, specially designed to cater to both students and experienced working professionals. This machine learning tutorial helps you gain a solid introduction to the fundamentals of machine learning and explore a wide range of techniques, including supervised, unsupervised, and reinforcement learning. What is Machine Learning? Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.  Recent Articles on Machine Learning      Introduction   Data and it’s Processing   Supervised Learning   Unsupervised Learning   Reinforcement Learning       Dimensionality Reduction   Natural Language Processing   Neural Networks   ML – Deployment   ML – Applications   Miscellaneous     Features of Machine learning  Machine learning is data driven technology. Large amount of data generated by organizations on daily bases. So, by notable relationships in data, organizations makes better decisions. Machine can learn itself from past data and automatically improve. From the given dataset it detects various patterns on data. For the big organizations branding is important and it will become more easy to target relatable customer base. It is similar to data mining because it is also deals with the huge amount of data.    Introduction :    Getting Started with Machine Learning   An Introduction to Machine Learning   What is Machine Learning ?   Introduction to Data in Machine Learning   Demystifying Machine Learning   ML – Applications   Best Python libraries for Machine Learning   Artificial Intelligence | An Introduction   Machine Learning and Artificial Intelligence   Difference between Machine learning and Artificial Intelligence   Agents in Artificial Intelligence   10 Basic Machine Learning Interview Questions      Data and It’s Processing:    Introduction to Data in Machine Learning   Understanding Data Processing   Python | Create Test DataSets using Sklearn   Python | Generate test datasets for Machine learning   Python | Data Preprocessing in Python   Data Cleaning   Feature Scaling – Part 1   Feature Scaling – Part 2   Python | Label Encoding of datasets   Python | One Hot Encoding of datasets   Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python   Dummy variable trap in Regression Models      Supervised learning :    Getting started with Classification   Basic Concept of Classification   Types of Regression Techniques   Classification vs Regression   ML | Types of Learning – Supervised Learning   Multiclass classification using scikit-learn    Gradient Descent :  Gradient Descent algorithm and its variants   Stochastic Gradient Descent (SGD)   Mini-Batch Gradient Descent with Python   Optimization techniques for Gradient Descent   Introduction to Momentum-based Gradient Optimizer      Linear Regression :  Introduction to Linear Regression   Gradient Descent in Linear Regression   Mathematical explanation for Linear Regression working   Normal Equation in Linear Regression   Linear Regression (Python Implementation)   Simple Linear-Regression using R   Univariate Linear Regression in Python   Multiple Linear Regression using Python   Multiple Linear Regression using R   Locally weighted Linear Regression   Generalized Linear Models   Python | Linear Regression using sklearn   Linear Regression Using Tensorflow   A Practical approach to Simple Linear Regression using R   Linear Regression using PyTorch   Pyspark | Linear regression using Apache MLlib    ML | Boston Housing Kaggle Challenge with Linear Regression     Python | Implementation of Polynomial Regression     Softmax Regression using TensorFlow    Logistic Regression :  Understanding Logistic Regression   Why Logistic Regression in Classification ?   Logistic Regression using Python   Cost function in Logistic Regression   Logistic Regression using Tensorflow      Naive Bayes Classifiers    Support Vector:  Support Vector Machines(SVMs) in Python   SVM Hyperparameter Tuning using GridSearchCV   Support Vector Machines(SVMs) in R   Using SVM to perform classification on a non-linear dataset      Decision Tree:  Decision Tree   Decision Tree Regression using sklearn   Decision Tree Introduction with example   Decision tree implementation using Python   Decision Tree in Software Engineering      Random Forest:  Random Forest Regression in Python   Ensemble Classifier   Voting Classifier using Sklearn    Bagging classifier        Unsupervised learning :    ML | Types of Learning – Unsupervised Learning   Supervised and Unsupervised learning   Clustering in Machine Learning   Different Types of Clustering Algorithm   K means Clustering – Introduction   Elbow Method for optimal value of k in KMeans   Random Initialization Trap in K-Means   ML | K-means++ Algorithm   Analysis of test data using K-Means Clustering in Python   Mini Batch K-means clustering algorithm   Mean-Shift Clustering   DBSCAN – Density based clustering   Implementing DBSCAN algorithm using Sklearn   Fuzzy Clustering   Spectral Clustering   OPTICS Clustering   OPTICS Clustering Implementing using Sklearn   Hierarchical clustering (Agglomerative and Divisive clustering)   Implementing Agglomerative Clustering using Sklearn   Gaussian Mixture Model      Reinforcement Learning:    Reinforcement learning   Reinforcement Learning Algorithm : Python Implementation using Q-learning   Introduction to Thompson Sampling   Genetic Algorithm for Reinforcement Learning   SARSA Reinforcement Learning   Q-Learning in Python      Dimensionality Reduction :    Introduction to Dimensionality Reduction   Introduction to Kernel PCA   Principal Component Analysis(PCA)   Principal Component Analysis with Python   Low-Rank Approximations   Overview of Linear Discriminant Analysis (LDA) Mathematical Explanation of Linear Discriminant Analysis (LDA)  Generalized Discriminant Analysis (GDA)   Independent Component Analysis   Feature Mapping   Extra Tree Classifier for Feature Selection   Chi-Square Test for Feature Selection – Mathematical Explanation   ML | T-distributed Stochastic Neighbor Embedding (t-SNE) Algorithm   Python | How and where to apply Feature Scaling?   Parameters for Feature Selection   Underfitting and Overfitting in Machine Learning      Natural Language Processing :    Introduction to Natural Language Processing   Text Preprocessing in Python | Set – 1   Text Preprocessing in Python | Set 2   Removing stop words with NLTK in Python   Tokenize text using NLTK in python   How tokenizing text, sentence, words works   Introduction to Stemming   Stemming words with NLTK   Lemmatization with NLTK   Lemmatization with TextBlob   How to get synonyms/antonyms from NLTK WordNet in Python?      Neural Networks :    Introduction to Artificial Neutral Networks | Set 1   Introduction to Artificial Neural Network | Set 2   Introduction to ANN (Artificial Neural Networks) | Set 3 (Hybrid Systems)   Introduction to ANN | Set 4 (Network Architectures)   Activation functions   Implementing Artificial Neural Network training process in Python   A single neuron neural network in Python    Convolutional Neural Networks  Introduction to Convolution Neural Network   Introduction to Pooling Layer   Introduction to Padding   Types of padding in convolution layer   Applying Convolutional Neural Network on mnist dataset      Recurrent Neural Networks  Introduction to Recurrent Neural Network   Recurrent Neural Networks Explanation   seq2seq model   Introduction to Long Short Term Memory   Long Short Term Memory Networks Explanation   Gated Recurrent Unit Networks(GAN)   Text Generation using Gated Recurrent Unit Networks      GANs – Generative Adversarial Network  Introduction to Generative Adversarial Network   Generative Adversarial Networks (GANs)   Use Cases of Generative Adversarial Networks   Building a Generative Adversarial Network using Keras   Modal Collapse in GANs     Introduction to Deep Q-Learning   Implementing Deep Q-Learning using Tensorflow      ML – Deployment :    Deploy your Machine Learning web app (Streamlit) on Heroku   Deploy a Machine Learning Model using Streamlit Library   Deploy Machine Learning Model using Flask   Python – Create UIs for prototyping Machine Learning model with Gradio   How to Prepare Data Before Deploying a Machine Learning Model?   Deploying ML Models as API using FastAPI    Deploying Scrapy spider on ScrapingHub      ML – Applications :    Rainfall prediction using Linear regression   Identifying handwritten digits using Logistic Regression in PyTorch   Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression   Python | Implementation of Movie Recommender System   Support Vector Machine to recognize facial features in C++   Decision Trees – Fake (Counterfeit) Coin Puzzle (12 Coin Puzzle)   Credit Card Fraud Detection   NLP analysis of Restaurant reviews   Applying Multinomial Naive Bayes to NLP Problems   Image compression using K-means clustering   Deep learning | Image Caption Generation using the Avengers EndGames Characters   How Does Google Use Machine Learning?   How Does NASA Use Machine Learning?   5 Mind-Blowing Ways Facebook Uses Machine Learning    Targeted Advertising using Machine Learning   How Machine Learning Is Used by Famous Companies?      Misc :    Pattern Recognition | Introduction   Calculate Efficiency Of Binary Classifier   Logistic Regression v/s Decision Tree Classification   R vs Python in Datascience   Explanation of Fundamental Functions involved in A3C algorithm   Differential Privacy and Deep Learning   Artificial intelligence vs Machine Learning vs Deep Learning   Introduction to Multi-Task Learning(MTL) for Deep Learning   Top 10 Algorithms every Machine Learning Engineer should know   Azure Virtual Machine for Machine Learning   30 minutes to machine learning   What is AutoML in Machine Learning?   Confusion Matrix in Machine Learning    Prerequisites to learn machine learning  Knowledge of Linear equations, graphs of functions, statistics, Linear Algebra, Probability, Calculus etc. Any programming language knowledge like Python, C++, R are recommended. Q.1 What is Machine learning and how is it different from Deep learning ?  Answer:  Machine learning develop programs that can access data and learn from it. Deep learning is the sub domain of the machine learning. Deep learning supports automatic extraction of features from the raw data.  Q.2. What are the different type of machine learning algorithms ?  Answer:   Supervised algorithms: These are the algorithms which learn from the labelled data, e.g. images labelled with dog face or not. Algorithm depends on supervised or labelled data. e.g. regression, object detection, segmentation. Non-Supervised algorithms: These are the algorithms which learn from the non labelled data, e.g. bunch of images given to make a similar set of images. e.g. clustering, dimensionality reduction etc. Semi-Supervised algorithms: Algorithms that uses both supervised or non-supervised data. Majority portion of data use for these algorithms are not supervised data. e.g. anamoly detection.   Q.3. Why we use machine learning ?  Answer:  Machine learning is used to make decisions based on data. By modelling the algorithms on the bases of historical data, Algorithms find the patterns and relationships that are difficult for humans to detect. These patterns are now further use for the future references to predict solution of unseen problems.  Q.4. What is the difference between Artificial Intelligence and Machine learning ?  Answer:     Artificial Intelligence Machine Learning     Develop an intelligent system that perform variety of complex jobs. Construct machines that can only accomplish the jobs for which they have trained.   It works as a program that does smart work. The tasks systems machine takes data and learns from data.   AI has broad variety of applications. ML allows systems to learn new things from data.   AI leads wisdom. ML leads to knowledge.
Machine Learning Tutorial
https://www.geeksforgeeks.org/machine-learning/