easy machine learning book
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The Hundred-Page Machine Learning Book: Burkov, Andriy: 9781999579500: Amazon.com: Books
2019-01-13
Andriy Burkov
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 The Hundred-Page Machine Learning Book

 by Andriy Burkov (Author)
 4.6 4.6 out of 5 stars 1,237 ratings
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 Peter Norvig , Research Director at Google, co-author of AIMA , the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field."

 Aurélien Géron , Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow : "The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field."

 Karolis Urbonas , Head of Data Science at Amazon : "A great introduction to machine learning from a world-class practitioner."

 Chao Han , VP, Head of R&D at Lucidworks : "I wish such a book existed when I was a statistics graduate student trying to learn about machine learning."

 Sujeet Varakhedi , Head of Engineering at eBay : "Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.''

 Deepak Agarwal , VP of Artificial Intelligence at LinkedIn : "A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.''

 Vincent Pollet , Head of Research at Nuance : "The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.''

 Gareth James , Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R : "This is a compact “how to do data science” manual and I predict it will become a go-to resource for academics and practitioners alike. At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests. There is also no shortage of details on the various approaches and the interested reader can gain further information on any particular method via the innovative companion book wiki. The book does not assume any high level mathematical or statistical training or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. It should certainly be required reading for anyone starting a PhD program in this area and will serve as a useful reference as they progress further. Finally, the book illustrates some of the algorithms using Python code, one of the most popular coding languages for machine learning. I would highly recommend “The Hundred-Page Machine Learning Book” for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base."

 Everything you really need to know in Machine Learning in a hundred pages.

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 1. ISBN-10
 199957950X
 2. ISBN-13
 978-1999579500
 3. Publication date
 January 13, 2019
 4. Language
 English
 5. Dimensions
 7.5 x 0.38 x 9.25 inches
 6. Print length
 160 pages
 7. See all details
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 Editorial Reviews

 Review

 "This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori. The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue. A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time going through a formal degree program." -- Deepak Agarwal , VP of Artificial Intelligence at LinkedIn

 "This book is a great introduction to machine learning from a world-class practitioner and LinkedIn superstar Andriy Burkov. He managed to find a good balance between the math of the algorithms, intuitive visualizations, and easy-to-read explanations. This book will benefit the newcomers to the field as a thorough introduction to the fundamentals of machine learning, while the experienced professionals will definitely enjoy the practical recommendations from Andriy's rich experience in the field." -- Karolis Urbonas , Head of Data Science at Amazon

 "I wish such a book existed when I was a statistics graduate student trying to learn about machine learning. There is the right amount of math which demystify the centerpiece of an algorithm with succinct but very clear descriptions. I'm also impressed by the widespread coverage and good choices of important methods as an introductory book (not all machine learning books mention things like learning to rank or metric learning). Highly recommended to STEM major students." -- Chao Han , VP, Head of R&D at Lucidworks

 "This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori. The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue. A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time going through a formal degree program." -- Sujeet Varakhedi , Head of Engineering at eBay

 "The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning. In his book, Andriy Burkov distills the ubiquitous material on Machine Learning into concise and well-balanced intuitive, theoretical and practical elements that bring beginners, managers, and practitioners many life hacks."-- Vincent Pollet , Head of Research at Nuance

 From the Author


 This long-awaited day has finally come and I'm proud and happy to announce that The Hundred-Page Machine Learning Book is now available to order in a high-quality color paperback edition as well as a Kindle edition.
 For three months, I worked hard to write a book that will make a difference. I firmly believe that I succeeded.
 I'm so sure about that because I received dozens of positive feedback. Both from readers who just start in artificial intelligence and from respected industry leaders. I'm extremely proud that such best-selling AI book authors and talented scientists as Peter Norvig and Aurélien Géron endorsed my book and wrote the texts for its back cover and that Gareth James wrote the Foreword.
 This book wouldn't be of such high quality without the help of volunteering readers who sent me hundreds of text improvement suggestions. The names of all volunteers can be found in the Acknowledgments section of the book.

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 Product details

 * Publisher ‏ : ‎ Andriy Burkov (January 13, 2019)
 * Language ‏ : ‎ English
 * Paperback ‏ : ‎ 160 pages
 * ISBN-10 ‏ : ‎ 199957950X
 * ISBN-13 ‏ : ‎ 978-1999579500
 * Item Weight ‏ : ‎ 13.8 ounces
 * Dimensions ‏ : ‎ 7.5 x 0.38 x 9.25 inches
 * Best Sellers Rank: #260,273 in Books ( See Top 100 in Books )
 + #47 in Machine Theory (Books)
 + #98 in Computer Neural Networks
 + #486 in Artificial Intelligence & Semantics
 * Customer Reviews:
 4.6 4.6 out of 5 stars 1,237 ratings
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 Andriy Burkov

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 Andriy Burkov is a dad of two and a machine learning expert based in Quebec City, Canada. Eleven years ago, he got a Ph.D. in Artificial Intelligence, and for the last eight years, he's been leading a team of machine learning developers at Gartner.

 His specialty is natural language processing. His team works on building state-of-the-art multilingual text extraction and normalization systems for production, using both shallow and deep learning technologies.

  Customer reviews

  4.6 out of 5 stars
  4.6 out of 5
  1,237 global ratings
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  4 star 12%
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  4 Stars
  Great book to gain an overview of Machine Learning
  In a small number of pages, the book covers many topics, and it is a good introduction for beginners or people managing a team of data scientists. On the flip side, it lacks depth, being more like a general summary on the topic. The value is that it explains a lot of methods and good practices, so many that even the expert is bound to learn something new, about some popular topic he heard about but only has a vague idea of what it means. This book will fill this gap.For in-depth coverage of selected modern machine learning topics with new research results focused on applications and a unified approach, with plenty of Python code yet 150 pages total, I suggest checking out my book "Intuitive Machine Learning and Explainable AI", also self-published.Such books are very well rendered in the PDF version, however the print version does not give them justice. Mine will never be printed, and if by chance it ever does, it will be on high quality paper and in color. Even then, a print version will always lack the HTML-like navigation features available in online versions.Compared to other similar books, at least Andriy's book has high-quality figures printer in color, and well rendered on paper. However, there is no biography or external references.
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  Matthew Emerick
  5.0 out of 5 stars Concise Overview to Get You Started
  Reviewed in the United States on February 19, 2020
  Verified Purchase
  Disclaimer: The author asked me to review this book, though I had already purchased my own copy. I promise to be 100% honest in how I feel about this book, both the good and the less so.

  Overview:
  This book does exactly what it states. It's a 100+ page book that gives you an overview of machine learning, the math behind most of the reviewed techniques so you can follow along with current research to an extent), and QR code links to further reading. The author also follows a 'read first, buy later' policy, which I respect.

  The book is very well organized, giving the reader an introduction and discussion on the mathematical notation used, a well written chapter that discusses several very common algorithms, talks about best practices (like feature engineering, breaking up the data into multiple sets, and tuning the model's hyperparameters), digs deeper into supervised learning, discusses unsupervised learning, and gives you a taste of a variety of other related topics.

  What I Like:
  This is a well rounded book, far more so than most books I've read on machine learning or artificial intelligence. After reading through this, I feel like I can competently discuss the subject, read one of the simpler machine learning research papers, and not be totally lost on the mathematics involved. The language used is concise and reads very well, showing very tight editing.

  What I Didn't Care For:
  I know that this is a general introduction and meant to be kept short. Like many other reviewers, however, I would have enjoyed a deeper look into everything that was in this book.

  What I Would Like To See:
  I know that the author is currently writing a data engineering book without the 100 page limitation. Personally, I would like to see him write a ML Math book (I'm weird like that) as well as an MLOps book. I expect the later to be what he writes next, if he chooses to continue writing.

  Overall, I got a LOT out of this book and look forward to more. I am giving a rating of 4.8 out of 5. If I include the wiki and further reading, I would bump it up to 4.9.
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  Peter Mancini
  5.0 out of 5 stars The best primer yet for modern machine learning
  Reviewed in the United States on August 7, 2019
  Verified Purchase
  In 2018 I was nominated for Data Scientist of the Year for Nashville, and became a finalist. Machine learning has been a big part of my career since 2005. I would like to say I don’t need a book like THPMLB, however, it is a fantastic resource. There is math in this book, and the reader will want to find a resource to become familiar with the notation, if they don’t already know it. I’ve always found the notation is the first filter for people getting into science of any type. However, the notation is more complex looking than it is once you know what the symbols mean. In the end, all math devolves down to a few basic operations and notation simply implies several assumptions about how to apply them.

  What makes the book excellent is the very plain-speaking method in which the lessons are taught. This isn’t someone trying to impress PhD’s in their field. This is someone looking to talk to you about machine learning. This straightforward approach will help you learn machine learning techniques.

  For someone like me, it offers a handy reference to techniques I’ve used but forgot the details to, or techniques I’ve wanted to explore. Its small size makes it easy to find what you are looking for, get a basic understanding, and launch you into further learning.

  Finally, this is a living product and there is a constantly updated wiki for this book. That alone is worth the small price you’d pay for this book. This book gets my highest recommendation.
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  Leonard B.
  5.0 out of 5 stars Software engineers, this is the machine learning book you need
  Reviewed in the United States on January 17, 2019
  Verified Purchase
  WARNING: It's now possible to buy a counterfeit copy of Andriy's book! To avoid that, click on "See All Buying Options" button and choose *Amazon.com* as the seller.

  I'm a software engineer currently working for a big tech company. This is hands down the book you need to grok and master machine learning concepts. As a programmer, I have felt capable of utilizing the machine learning tools available, but have felt distant from understanding the many cited academic papers. I can confidently say, just a few chapters into this book, that this is the book I was missing!

  I have followed Andriy on LinkedIn for a long time now, and always appreciated his posts. When I saw he was publishing a book, I didn't think twice and ordered it. As expected, the book is clear, concise and does a thorough job explaining basic mathematical concepts, machine learning principles, and the most important fundamentals to understand the field.

  One note: I can tell this book will be useful for a long time. I have many tech related books that become obsolete a few years or even months after they are published. Andriy's approach delves into the core principles, while explaining how to understand further developments into the field. This is something I was missing and truly appreciate.

  I have a quirk of reading physical books alongside a text-to-speech interface on a digital device. Especially with text books, this is helpful, but not all textbooks are capable of being processed this way. Fortunately, when you purchase the physical book, Andriy sends you the digital edition as well. As a result, I have been able to breeze through the text in my ideal learning state.

  For those who have been working around the academic machine learning world, but are influenced by it - buy this book! For those who are familiar with machine learning concepts and have gone through all the blog posts and MOOCs you could get your hands on - buy this book!
  Read more
  Leonard B.
  5.0 out of 5 stars Software engineers, this is the machine learning book you need
  Reviewed in the United States on January 17, 2019
  WARNING: It's now possible to buy a counterfeit copy of Andriy's book! To avoid that, click on "See All Buying Options" button and choose *Amazon.com* as the seller.

  I'm a software engineer currently working for a big tech company. This is hands down the book you need to grok and master machine learning concepts. As a programmer, I have felt capable of utilizing the machine learning tools available, but have felt distant from understanding the many cited academic papers. I can confidently say, just a few chapters into this book, that this is the book I was missing!

  I have followed Andriy on LinkedIn for a long time now, and always appreciated his posts. When I saw he was publishing a book, I didn't think twice and ordered it. As expected, the book is clear, concise and does a thorough job explaining basic mathematical concepts, machine learning principles, and the most important fundamentals to understand the field.

  One note: I can tell this book will be useful for a long time. I have many tech related books that become obsolete a few years or even months after they are published. Andriy's approach delves into the core principles, while explaining how to understand further developments into the field. This is something I was missing and truly appreciate.

  I have a quirk of reading physical books alongside a text-to-speech interface on a digital device. Especially with text books, this is helpful, but not all textbooks are capable of being processed this way. Fortunately, when you purchase the physical book, Andriy sends you the digital edition as well. As a result, I have been able to breeze through the text in my ideal learning state.

  For those who have been working around the academic machine learning world, but are influenced by it - buy this book! For those who are familiar with machine learning concepts and have gone through all the blog posts and MOOCs you could get your hands on - buy this book!
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  Fabiana Gonçalves
  5.0 out of 5 stars Cumpre totalmente o que promete
  Reviewed in Brazil on September 17, 2022
  Verified Purchase
  O conteúdo realmente superou minhas expectativas. Em pouco mais de 100 páginas, o livro consegue trazer os principais conteúdos que qualquer pessoa que trabalha ou deseja trabalhar com Machine Learning deve saber. Apesar da complexidade dos temas, o autor é bastante didático e objetivo. O leitor que não tem conhecimento em disciplinas do ciclo básico da maioria dos cursos de exatas, como Cálculo, Álgebra Linear, Geometria Analítica ou Estatística, pode sentir um pouco de dificuldade para compreender alguns temas, mas isso não deve ser impeditivo para adquirir o livro. Na medida do possível, o autor tenta explicar um pouco desses pré-requisitos em cada assunto. Mas mais importante do que isso, certamente o leitor pode se motivar em aprender sobre essas disciplinas.
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  D. Lingman
  5.0 out of 5 stars Very good, concise summary of ML
  Reviewed in Canada on March 29, 2022
  Verified Purchase
  Just enough math to make sure you understand why it's doing what it does, but not filled with page after page of linear algebra. Good balance was picked.
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  Alessio
  5.0 out of 5 stars Tosto
  Reviewed in Italy on February 29, 2024
  Verified Purchase
  Un libro tosto come il ferro, necessario come l’acqua e l’aria. Chiaro, conciso, preciso. Mena durissimo.
  Io ve l’ho detto.
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  Sayan Bhowmick
  5.0 out of 5 stars Highly Recommended
  Reviewed in India on February 12, 2024
  Verified Purchase
  The best book to learn ML concepts in a easy way. I loved this book & I also highly recommend this book
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  H.P.J.M.
  5.0 out of 5 stars I admire what the author achieved here
  Reviewed in the United Kingdom on October 27, 2023
  Verified Purchase
  The advantage of short books like this is that if they are well written the author has to think carefully about what to write and how to write it. That's certainly been done here.

  After a crash course in what ML is and some mathematical notation, a few popular ML algorithms are introduced, before Burkov takes a look at what a learning algorithm fundamentally does: optimising a particular function (normally by minimising a loss function).

  Other parts of the book go into ML practice, deep learning, practical problems and solutions, and tips and tricks for situations you might run into (e.g. handling multiple outputs). Unsupervised learning, word embeddings and ranking and recommendation systems are discussed. The book's conclusion talks about other areas to learn about which weren't present.

  The book is dense in parts, no doubt about it. Burkov lays down all the mathematical formulae but also explains things pretty well and touches on the intuition behind key ideas, along with useful pictures and diagrams.

  That is one of the things I liked the most: it is rigorous, concise, but not unclear. Another thing I really liked is that it touches on very practical problem discussed less frequently elsewhere (e.g. imbalanced datasets) and interesting approaches you won't find in more traditional resources (like one and zero shot learning).

  In contrast to what some other reviewers on the back of book say, I'd say that this book is probably not the best one for absolute beginners. It would be much more useful when you know what ML is and have done a project or two, at least.

  To sum up, if you want an information packed ML book that has both theory and useful practical tips, read this.
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 The Hundred-Page Machine Learning Book
 by Andriy Burkov (Author)
 4.6 4.6 out of 5 stars 1,237 ratings
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 Peter Norvig , Research Director at Google, co-author of AIMA , the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field."
 Aurélien Géron , Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow : "The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field."
 Karolis Urbonas , Head of Data Science at Amazon : "A great introduction to machine learning from a world-class practitioner."
 Chao Han , VP, Head of R&D at Lucidworks : "I wish such a book existed when I was a statistics graduate student trying to learn about machine learning."
 Sujeet Varakhedi , Head of Engineering at eBay : "Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.''
 Deepak Agarwal , VP of Artificial Intelligence at LinkedIn : "A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.''
 Vincent Pollet , Head of Research at Nuance : "The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.''
 Gareth James , Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R : "This is a compact “how to do data science” manual and I predict it will become a go-to resource for academics and practitioners alike. At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests. There is also no shortage of details on the various approaches and the interested reader can gain further information on any particular method via the innovative companion book wiki. The book does not assume any high level mathematical or statistical training or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. It should certainly be required reading for anyone starting a PhD program in this area and will serve as a useful reference as they progress further. Finally, the book illustrates some of the algorithms using Python code, one of the most popular coding languages for machine learning. I would highly recommend “The Hundred-Page Machine Learning Book” for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base."
 Everything you really need to know in Machine Learning in a hundred pages.
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 1. ISBN-10
 199957950X
 2. ISBN-13
 978-1999579500
 3. Publication date
 January 13, 2019
 4. Language
 English
 5. Dimensions
 7.5 x 0.38 x 9.25 inches
 6. Print length
 160 pages
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 Editorial Reviews
 Review
 "This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori. The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue. A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time going through a formal degree program." -- Deepak Agarwal , VP of Artificial Intelligence at LinkedIn
 "This book is a great introduction to machine learning from a world-class practitioner and LinkedIn superstar Andriy Burkov. He managed to find a good balance between the math of the algorithms, intuitive visualizations, and easy-to-read explanations. This book will benefit the newcomers to the field as a thorough introduction to the fundamentals of machine learning, while the experienced professionals will definitely enjoy the practical recommendations from Andriy's rich experience in the field." -- Karolis Urbonas , Head of Data Science at Amazon
 "I wish such a book existed when I was a statistics graduate student trying to learn about machine learning. There is the right amount of math which demystify the centerpiece of an algorithm with succinct but very clear descriptions. I'm also impressed by the widespread coverage and good choices of important methods as an introductory book (not all machine learning books mention things like learning to rank or metric learning). Highly recommended to STEM major students." -- Chao Han , VP, Head of R&D at Lucidworks
 "This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori. The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue. A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time going through a formal degree program." -- Sujeet Varakhedi , Head of Engineering at eBay
 "The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning. In his book, Andriy Burkov distills the ubiquitous material on Machine Learning into concise and well-balanced intuitive, theoretical and practical elements that bring beginners, managers, and practitioners many life hacks."-- Vincent Pollet , Head of Research at Nuance
 From the Author
 This long-awaited day has finally come and I'm proud and happy to announce that The Hundred-Page Machine Learning Book is now available to order in a high-quality color paperback edition as well as a Kindle edition.
 For three months, I worked hard to write a book that will make a difference. I firmly believe that I succeeded.
 I'm so sure about that because I received dozens of positive feedback. Both from readers who just start in artificial intelligence and from respected industry leaders. I'm extremely proud that such best-selling AI book authors and talented scientists as Peter Norvig and Aurélien Géron endorsed my book and wrote the texts for its back cover and that Gareth James wrote the Foreword.
 This book wouldn't be of such high quality without the help of volunteering readers who sent me hundreds of text improvement suggestions. The names of all volunteers can be found in the Acknowledgments section of the book.
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 * Publisher ‏ : ‎ Andriy Burkov (January 13, 2019)
 * Language ‏ : ‎ English
 * Paperback ‏ : ‎ 160 pages
 * ISBN-10 ‏ : ‎ 199957950X
 * ISBN-13 ‏ : ‎ 978-1999579500
 * Item Weight ‏ : ‎ 13.8 ounces
 * Dimensions ‏ : ‎ 7.5 x 0.38 x 9.25 inches
 * Best Sellers Rank: #260,273 in Books ( See Top 100 in Books )
 + #47 in Machine Theory (Books)
 + #98 in Computer Neural Networks
 + #486 in Artificial Intelligence & Semantics
 * Customer Reviews:
 4.6 4.6 out of 5 stars 1,237 ratings
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 Andriy Burkov is a dad of two and a machine learning expert based in Quebec City, Canada. Eleven years ago, he got a Ph.D. in Artificial Intelligence, and for the last eight years, he's been leading a team of machine learning developers at Gartner.
 His specialty is natural language processing. His team works on building state-of-the-art multilingual text extraction and normalization systems for production, using both shallow and deep learning technologies.
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  4 Stars
  Great book to gain an overview of Machine Learning
  In a small number of pages, the book covers many topics, and it is a good introduction for beginners or people managing a team of data scientists. On the flip side, it lacks depth, being more like a general summary on the topic. The value is that it explains a lot of methods and good practices, so many that even the expert is bound to learn something new, about some popular topic he heard about but only has a vague idea of what it means. This book will fill this gap.For in-depth coverage of selected modern machine learning topics with new research results focused on applications and a unified approach, with plenty of Python code yet 150 pages total, I suggest checking out my book "Intuitive Machine Learning and Explainable AI", also self-published.Such books are very well rendered in the PDF version, however the print version does not give them justice. Mine will never be printed, and if by chance it ever does, it will be on high quality paper and in color. Even then, a print version will always lack the HTML-like navigation features available in online versions.Compared to other similar books, at least Andriy's book has high-quality figures printer in color, and well rendered on paper. However, there is no biography or external references.
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  Matthew Emerick
  5.0 out of 5 stars Concise Overview to Get You Started
  Reviewed in the United States on February 19, 2020
  Verified Purchase
  Disclaimer: The author asked me to review this book, though I had already purchased my own copy. I promise to be 100% honest in how I feel about this book, both the good and the less so.
  Overview:
  This book does exactly what it states. It's a 100+ page book that gives you an overview of machine learning, the math behind most of the reviewed techniques so you can follow along with current research to an extent), and QR code links to further reading. The author also follows a 'read first, buy later' policy, which I respect.
  The book is very well organized, giving the reader an introduction and discussion on the mathematical notation used, a well written chapter that discusses several very common algorithms, talks about best practices (like feature engineering, breaking up the data into multiple sets, and tuning the model's hyperparameters), digs deeper into supervised learning, discusses unsupervised learning, and gives you a taste of a variety of other related topics.
  What I Like:
  This is a well rounded book, far more so than most books I've read on machine learning or artificial intelligence. After reading through this, I feel like I can competently discuss the subject, read one of the simpler machine learning research papers, and not be totally lost on the mathematics involved. The language used is concise and reads very well, showing very tight editing.
  What I Didn't Care For:
  I know that this is a general introduction and meant to be kept short. Like many other reviewers, however, I would have enjoyed a deeper look into everything that was in this book.
  What I Would Like To See:
  I know that the author is currently writing a data engineering book without the 100 page limitation. Personally, I would like to see him write a ML Math book (I'm weird like that) as well as an MLOps book. I expect the later to be what he writes next, if he chooses to continue writing.
  Overall, I got a LOT out of this book and look forward to more. I am giving a rating of 4.8 out of 5. If I include the wiki and further reading, I would bump it up to 4.9.
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  Peter Mancini
  5.0 out of 5 stars The best primer yet for modern machine learning
  Reviewed in the United States on August 7, 2019
  Verified Purchase
  In 2018 I was nominated for Data Scientist of the Year for Nashville, and became a finalist. Machine learning has been a big part of my career since 2005. I would like to say I don’t need a book like THPMLB, however, it is a fantastic resource. There is math in this book, and the reader will want to find a resource to become familiar with the notation, if they don’t already know it. I’ve always found the notation is the first filter for people getting into science of any type. However, the notation is more complex looking than it is once you know what the symbols mean. In the end, all math devolves down to a few basic operations and notation simply implies several assumptions about how to apply them.
  What makes the book excellent is the very plain-speaking method in which the lessons are taught. This isn’t someone trying to impress PhD’s in their field. This is someone looking to talk to you about machine learning. This straightforward approach will help you learn machine learning techniques.
  For someone like me, it offers a handy reference to techniques I’ve used but forgot the details to, or techniques I’ve wanted to explore. Its small size makes it easy to find what you are looking for, get a basic understanding, and launch you into further learning.
  Finally, this is a living product and there is a constantly updated wiki for this book. That alone is worth the small price you’d pay for this book. This book gets my highest recommendation.
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  Leonard B.
  5.0 out of 5 stars Software engineers, this is the machine learning book you need
  Reviewed in the United States on January 17, 2019
  Verified Purchase
  WARNING: It's now possible to buy a counterfeit copy of Andriy's book! To avoid that, click on "See All Buying Options" button and choose *Amazon.com* as the seller.
  I'm a software engineer currently working for a big tech company. This is hands down the book you need to grok and master machine learning concepts. As a programmer, I have felt capable of utilizing the machine learning tools available, but have felt distant from understanding the many cited academic papers. I can confidently say, just a few chapters into this book, that this is the book I was missing!
  I have followed Andriy on LinkedIn for a long time now, and always appreciated his posts. When I saw he was publishing a book, I didn't think twice and ordered it. As expected, the book is clear, concise and does a thorough job explaining basic mathematical concepts, machine learning principles, and the most important fundamentals to understand the field.
  One note: I can tell this book will be useful for a long time. I have many tech related books that become obsolete a few years or even months after they are published. Andriy's approach delves into the core principles, while explaining how to understand further developments into the field. This is something I was missing and truly appreciate.
  I have a quirk of reading physical books alongside a text-to-speech interface on a digital device. Especially with text books, this is helpful, but not all textbooks are capable of being processed this way. Fortunately, when you purchase the physical book, Andriy sends you the digital edition as well. As a result, I have been able to breeze through the text in my ideal learning state.
  For those who have been working around the academic machine learning world, but are influenced by it - buy this book! For those who are familiar with machine learning concepts and have gone through all the blog posts and MOOCs you could get your hands on - buy this book!
  Read more
  Leonard B.
  5.0 out of 5 stars Software engineers, this is the machine learning book you need
  Reviewed in the United States on January 17, 2019
  WARNING: It's now possible to buy a counterfeit copy of Andriy's book! To avoid that, click on "See All Buying Options" button and choose *Amazon.com* as the seller.
  I'm a software engineer currently working for a big tech company. This is hands down the book you need to grok and master machine learning concepts. As a programmer, I have felt capable of utilizing the machine learning tools available, but have felt distant from understanding the many cited academic papers. I can confidently say, just a few chapters into this book, that this is the book I was missing!
  I have followed Andriy on LinkedIn for a long time now, and always appreciated his posts. When I saw he was publishing a book, I didn't think twice and ordered it. As expected, the book is clear, concise and does a thorough job explaining basic mathematical concepts, machine learning principles, and the most important fundamentals to understand the field.
  One note: I can tell this book will be useful for a long time. I have many tech related books that become obsolete a few years or even months after they are published. Andriy's approach delves into the core principles, while explaining how to understand further developments into the field. This is something I was missing and truly appreciate.
  I have a quirk of reading physical books alongside a text-to-speech interface on a digital device. Especially with text books, this is helpful, but not all textbooks are capable of being processed this way. Fortunately, when you purchase the physical book, Andriy sends you the digital edition as well. As a result, I have been able to breeze through the text in my ideal learning state.
  For those who have been working around the academic machine learning world, but are influenced by it - buy this book! For those who are familiar with machine learning concepts and have gone through all the blog posts and MOOCs you could get your hands on - buy this book!
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  Fabiana Gonçalves
  5.0 out of 5 stars Cumpre totalmente o que promete
  Reviewed in Brazil on September 17, 2022
  Verified Purchase
  O conteúdo realmente superou minhas expectativas. Em pouco mais de 100 páginas, o livro consegue trazer os principais conteúdos que qualquer pessoa que trabalha ou deseja trabalhar com Machine Learning deve saber. Apesar da complexidade dos temas, o autor é bastante didático e objetivo. O leitor que não tem conhecimento em disciplinas do ciclo básico da maioria dos cursos de exatas, como Cálculo, Álgebra Linear, Geometria Analítica ou Estatística, pode sentir um pouco de dificuldade para compreender alguns temas, mas isso não deve ser impeditivo para adquirir o livro. Na medida do possível, o autor tenta explicar um pouco desses pré-requisitos em cada assunto. Mas mais importante do que isso, certamente o leitor pode se motivar em aprender sobre essas disciplinas.
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  D. Lingman
  5.0 out of 5 stars Very good, concise summary of ML
  Reviewed in Canada on March 29, 2022
  Verified Purchase
  Just enough math to make sure you understand why it's doing what it does, but not filled with page after page of linear algebra. Good balance was picked.
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  Alessio
  5.0 out of 5 stars Tosto
  Reviewed in Italy on February 29, 2024
  Verified Purchase
  Un libro tosto come il ferro, necessario come l’acqua e l’aria. Chiaro, conciso, preciso. Mena durissimo.
  Io ve l’ho detto.
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  Sayan Bhowmick
  5.0 out of 5 stars Highly Recommended
  Reviewed in India on February 12, 2024
  Verified Purchase
  The best book to learn ML concepts in a easy way. I loved this book & I also highly recommend this book
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  H.P.J.M.
  5.0 out of 5 stars I admire what the author achieved here
  Reviewed in the United Kingdom on October 27, 2023
  Verified Purchase
  The advantage of short books like this is that if they are well written the author has to think carefully about what to write and how to write it. That's certainly been done here.
  After a crash course in what ML is and some mathematical notation, a few popular ML algorithms are introduced, before Burkov takes a look at what a learning algorithm fundamentally does: optimising a particular function (normally by minimising a loss function).
  Other parts of the book go into ML practice, deep learning, practical problems and solutions, and tips and tricks for situations you might run into (e.g. handling multiple outputs). Unsupervised learning, word embeddings and ranking and recommendation systems are discussed. The book's conclusion talks about other areas to learn about which weren't present.
  The book is dense in parts, no doubt about it. Burkov lays down all the mathematical formulae but also explains things pretty well and touches on the intuition behind key ideas, along with useful pictures and diagrams.
  That is one of the things I liked the most: it is rigorous, concise, but not unclear. Another thing I really liked is that it touches on very practical problem discussed less frequently elsewhere (e.g. imbalanced datasets) and interesting approaches you won't find in more traditional resources (like one and zero shot learning).
  In contrast to what some other reviewers on the back of book say, I'd say that this book is probably not the best one for absolute beginners. It would be much more useful when you know what ML is and have done a project or two, at least.
  To sum up, if you want an information packed ML book that has both theory and useful practical tips, read this.
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The Hundred-Page Machine Learning Book: Burkov, Andriy: 9781999579500: Amazon.com: Books
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https://www.amazon.com/Machine-Learning-Hackers-Studies-Algorithms/dp/1449303714?tag=hackr-20&geniuslink=true
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started: Conway, Drew, White, John Myles: 9781449303716: Amazon.com: Books
2012-03-20
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 Machine Learning for Hackers: Case Studies and Algorithms to Get You Started First Edition

 by Drew Conway (Author), John Myles White
 3.7 3.7 out of 5 stars 62 ratings
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 If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning―a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.

 Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.

 * Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text
 * Use linear regression to predict the number of page views for the top 1,000 websites
 * Learn optimization techniques by attempting to break a simple letter cipher
 * Compare and contrast U.S. Senators statistically, based on their voting records
 * Build a “whom to follow” recommendation system from Twitter data
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 1. ISBN-10
 1449303714
 2. ISBN-13
 978-1449303716
 3. Edition
 First Edition
 4. Publisher
 O'Reilly
 5. Publication date
 March 20, 2012
 6. Language
 English
 7. Dimensions
 7 x 0.75 x 9.19 inches
 8. Print length
 320 pages
 9. See all details
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 Editorial Reviews

 About the Author

 Drew Conway is a PhD candidate in Politics at NYU. He studies international relations, conflict, and terrorism using the tools of mathematics, statistics, and computer science in an attempt to gain a deeper understanding of these phenomena. His academic curiosity is informed by his years as an analyst in the U.S. intelligence and defense communities.

 John Myles White is a PhD candidate in Psychology at Princeton. He studies pattern recognition, decision-making, and economic behavior using behavioral methods and fMRI. He is particularly interested in anomalies of value assessment.

 Product details

 * Publisher ‏ : ‎ O'Reilly; First Edition (March 20, 2012)
 * Language ‏ : ‎ English
 * Paperback ‏ : ‎ 320 pages
 * ISBN-10 ‏ : ‎ 1449303714
 * ISBN-13 ‏ : ‎ 978-1449303716
 * Item Weight ‏ : ‎ 1.14 pounds
 * Dimensions ‏ : ‎ 7 x 0.75 x 9.19 inches
 * Best Sellers Rank: #1,114,970 in Books ( See Top 100 in Books )
 + #208 in Machine Theory (Books)
 + #375 in Natural Language Processing (Books)
 + #740 in Computer Hacking
 * Customer Reviews:
 3.7 3.7 out of 5 stars 62 ratings
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  W. Hogan
  5.0 out of 5 stars An interesting and easy read
  Reviewed in the United States on December 12, 2013
  Verified Purchase
  I enjoyed reading this book.

  Pro's:
  The text is parsimonious.
  The examples are interesting.
  The coding is clever.
  The book is less expensive and easier to understand than most Springer texts.

  Con's:
  A substantial part of the code is peripheral tasks; this can be skipped.
  Some of the code is out of date.

  These Con's are trivial. The book is great. I would buy any other books written by these authors.
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  Graham
  2.0 out of 5 stars Entry Level R Programming Book
  Reviewed in the United States on November 13, 2016
  Verified Purchase
  This text offers a detailed description in each of 10 case studies about how to build a machine learning solution to the particular problem mentioned. The authors do this in R, and are extensively descriptive about the mechanics of writing R code. If you've never written a computer program, but want to understand how to implement a prewritten machine learning tool in R, this book could be of assistance. However, I'm not sure the authors actually understand the mathematical theory of machine learning; in this book they constantly substitute descriptions of how to select appropriate algorithm parameters with a trial-and-error approach, they do not explain how the algorithms work, and I'm not sure they ever mention the mechanics of "learning" with respect to mathematics. The book has brief passages about machine learning hidden amongst vast chapters about how to read computer directories and load data, etc. Again this book could be useful to the reader who understands ML, but not computer programming.
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  Chris
  4.0 out of 5 stars Learned something new
  Reviewed in the United States on October 2, 2012
  Verified Purchase
  I learned a little about the R language and how a lot of scientist of physicist use it the write functions to dynamically create graphs to analyze the data they compiled. It also encompasses a lot of the same math you learn in statistics classes which makes sense. I even ended up downloading an R language reference app from the Apple app store to have an object reference of the language.
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  Voracious Reader
  3.0 out of 5 stars Machine Learning for Non-Hackers
  Reviewed in the United States on March 21, 2012
  Verified Purchase
  By page count, this is primarily a book on R, with some additional time spent on machine learning.

  There is way too much time spent on R, dedicated to such things as parsing email messages, and spidering webpages, etc. These are things that no-one with other tools available would do in R. And it's not that it's easier to do it in R, it's actually harder than using an appropriate library, like JavaMail. And yet, while much time is spent in details, like regexes to extract dates (ick!), more interesting R functions are given short shrift.

  There's some good material in here, but it's buried under the weight of doing everything in R. If you are a non-programmer, and want to use only one hammer for everything, then R is not a bad choice. But it's not a good choice for developers that are already comfortable with a wider variety of tools.

  I'd recommend Programming Collective Intelligence by Segaran, if you would describe yourself as a "Hacker".
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  Ravi Aranke
  5.0 out of 5 stars Excellent and immediately practical, if you already know some R
  Reviewed in the United States on February 20, 2012
  I started my journey in the machine learning / data mining field thanks to curiosity generated by Toby Segaran's classic Programming Collective Intelligence: Building Smart Web 2.0 Applications . The book by Drew Conway and John White continues in the same excellent tradition. It presents case studies which are interesting enough that you can appreciate them without too much domain knowledge and without getting too deep into technical nitty-gritty. At the same time, the case studies are meaty enough that you can adapt them to real life problems and hack together a quick working prototype in your practice.

  By many estimates (and my own experience), 80% of time in machine learning is spent in data cleaning and exploratory data analysis. This book has very good coverage of both areas. Authors use Hadley Wickham's excellent packages viz. ggplot2, plyr and reshape2. If you are doing serious exploratory data analysis in R, these packages are a must and the book does a great job in showing them in action.

  The reason I suffixed the review with 'if you know a little R' is that data cleansing requires one to be fairly comfortable with somewhat arcane R syntax. If you don't know any R at all, it would be helpful to get a more gentle introduction - such as R Cookbook (O'Reilly Cookbooks) - before you tackle this book.

  In summary, this is an excellent 2nd book on R to have as you try to deploy machine learning in real life.
  BTW, if you are looking for 3rd R book, my vote is Data Mining with R: Learning with Case Studies (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
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  Martin Castillo Cabrera
  5.0 out of 5 stars Temas acordes
  Reviewed in Mexico on April 13, 2019
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  Excelente libro
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  Sai Sampath
  5.0 out of 5 stars The book is real good but I wouldn't prefer it if you are new ...
  Reviewed in India on December 13, 2017
  Verified Purchase
  The book is real good but I wouldn't prefer it if you are new to machine learning. You would find it to be real good if have basic knowledge of the subject
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  Carlo
  5.0 out of 5 stars Esauriente
  Reviewed in Italy on May 3, 2017
  Verified Purchase
  Un buon libro per chi si vuole avvicinare al Machine Learning, il taglio della spiegazione è americano quindi con esempi significativi.
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  Bobobob
  1.0 out of 5 stars Titel ist vorsätzliche Täuschung
  Reviewed in Germany on October 21, 2014
  Verified Purchase
  Kann ich nur dringend von abraten. Klare vorsätzliche Täuschung der Titel. Es wir einem nicht erklärt was Machine Learning ist, es wird nur R benutzt die Algorithmen zu berechnen oder zu visualisieren und es wird weder R noch maschinelles Lernen erklärt.
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  Kosako2007
  5.0 out of 5 stars Genial introducción al mundo del Machine Learning
  Reviewed in Spain on February 23, 2013
  Verified Purchase
  El libro es una muy buena introducción al Machine Learning y los conceptos en los que se basa. Lo recomiendo para todo aquel desarrollador que quiera introducirse en ese mundo.
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 Machine Learning for Hackers: Case Studies and Algorithms to Get You Started First Edition
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 If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning―a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.
 Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.
 * Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text
 * Use linear regression to predict the number of page views for the top 1,000 websites
 * Learn optimization techniques by attempting to break a simple letter cipher
 * Compare and contrast U.S. Senators statistically, based on their voting records
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 1449303714
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 978-1449303716
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 4. Publisher
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 5. Publication date
 March 20, 2012
 6. Language
 English
 7. Dimensions
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 8. Print length
 320 pages
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 About the Author
 Drew Conway is a PhD candidate in Politics at NYU. He studies international relations, conflict, and terrorism using the tools of mathematics, statistics, and computer science in an attempt to gain a deeper understanding of these phenomena. His academic curiosity is informed by his years as an analyst in the U.S. intelligence and defense communities.
 John Myles White is a PhD candidate in Psychology at Princeton. He studies pattern recognition, decision-making, and economic behavior using behavioral methods and fMRI. He is particularly interested in anomalies of value assessment.
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 * Publisher ‏ : ‎ O'Reilly; First Edition (March 20, 2012)
 * Language ‏ : ‎ English
 * Paperback ‏ : ‎ 320 pages
 * ISBN-10 ‏ : ‎ 1449303714
 * ISBN-13 ‏ : ‎ 978-1449303716
 * Item Weight ‏ : ‎ 1.14 pounds
 * Dimensions ‏ : ‎ 7 x 0.75 x 9.19 inches
 * Best Sellers Rank: #1,114,970 in Books ( See Top 100 in Books )
 + #208 in Machine Theory (Books)
 + #375 in Natural Language Processing (Books)
 + #740 in Computer Hacking
 * Customer Reviews:
 3.7 3.7 out of 5 stars 62 ratings
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  3.7 out of 5 stars
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  W. Hogan
  5.0 out of 5 stars An interesting and easy read
  Reviewed in the United States on December 12, 2013
  Verified Purchase
  I enjoyed reading this book.
  Pro's:
  The text is parsimonious.
  The examples are interesting.
  The coding is clever.
  The book is less expensive and easier to understand than most Springer texts.
  Con's:
  A substantial part of the code is peripheral tasks; this can be skipped.
  Some of the code is out of date.
  These Con's are trivial. The book is great. I would buy any other books written by these authors.
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  Graham
  2.0 out of 5 stars Entry Level R Programming Book
  Reviewed in the United States on November 13, 2016
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  This text offers a detailed description in each of 10 case studies about how to build a machine learning solution to the particular problem mentioned. The authors do this in R, and are extensively descriptive about the mechanics of writing R code. If you've never written a computer program, but want to understand how to implement a prewritten machine learning tool in R, this book could be of assistance. However, I'm not sure the authors actually understand the mathematical theory of machine learning; in this book they constantly substitute descriptions of how to select appropriate algorithm parameters with a trial-and-error approach, they do not explain how the algorithms work, and I'm not sure they ever mention the mechanics of "learning" with respect to mathematics. The book has brief passages about machine learning hidden amongst vast chapters about how to read computer directories and load data, etc. Again this book could be useful to the reader who understands ML, but not computer programming.
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  Chris
  4.0 out of 5 stars Learned something new
  Reviewed in the United States on October 2, 2012
  Verified Purchase
  I learned a little about the R language and how a lot of scientist of physicist use it the write functions to dynamically create graphs to analyze the data they compiled. It also encompasses a lot of the same math you learn in statistics classes which makes sense. I even ended up downloading an R language reference app from the Apple app store to have an object reference of the language.
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  Voracious Reader
  3.0 out of 5 stars Machine Learning for Non-Hackers
  Reviewed in the United States on March 21, 2012
  Verified Purchase
  By page count, this is primarily a book on R, with some additional time spent on machine learning.
  There is way too much time spent on R, dedicated to such things as parsing email messages, and spidering webpages, etc. These are things that no-one with other tools available would do in R. And it's not that it's easier to do it in R, it's actually harder than using an appropriate library, like JavaMail. And yet, while much time is spent in details, like regexes to extract dates (ick!), more interesting R functions are given short shrift.
  There's some good material in here, but it's buried under the weight of doing everything in R. If you are a non-programmer, and want to use only one hammer for everything, then R is not a bad choice. But it's not a good choice for developers that are already comfortable with a wider variety of tools.
  I'd recommend Programming Collective Intelligence by Segaran, if you would describe yourself as a "Hacker".
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  Ravi Aranke
  5.0 out of 5 stars Excellent and immediately practical, if you already know some R
  Reviewed in the United States on February 20, 2012
  I started my journey in the machine learning / data mining field thanks to curiosity generated by Toby Segaran's classic Programming Collective Intelligence: Building Smart Web 2.0 Applications . The book by Drew Conway and John White continues in the same excellent tradition. It presents case studies which are interesting enough that you can appreciate them without too much domain knowledge and without getting too deep into technical nitty-gritty. At the same time, the case studies are meaty enough that you can adapt them to real life problems and hack together a quick working prototype in your practice.
  By many estimates (and my own experience), 80% of time in machine learning is spent in data cleaning and exploratory data analysis. This book has very good coverage of both areas. Authors use Hadley Wickham's excellent packages viz. ggplot2, plyr and reshape2. If you are doing serious exploratory data analysis in R, these packages are a must and the book does a great job in showing them in action.
  The reason I suffixed the review with 'if you know a little R' is that data cleansing requires one to be fairly comfortable with somewhat arcane R syntax. If you don't know any R at all, it would be helpful to get a more gentle introduction - such as R Cookbook (O'Reilly Cookbooks) - before you tackle this book.
  In summary, this is an excellent 2nd book on R to have as you try to deploy machine learning in real life.
  BTW, if you are looking for 3rd R book, my vote is Data Mining with R: Learning with Case Studies (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
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  Martin Castillo Cabrera
  5.0 out of 5 stars Temas acordes
  Reviewed in Mexico on April 13, 2019
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  Excelente libro
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  Sai Sampath
  5.0 out of 5 stars The book is real good but I wouldn't prefer it if you are new ...
  Reviewed in India on December 13, 2017
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  The book is real good but I wouldn't prefer it if you are new to machine learning. You would find it to be real good if have basic knowledge of the subject
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  Carlo
  5.0 out of 5 stars Esauriente
  Reviewed in Italy on May 3, 2017
  Verified Purchase
  Un buon libro per chi si vuole avvicinare al Machine Learning, il taglio della spiegazione è americano quindi con esempi significativi.
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  Bobobob
  1.0 out of 5 stars Titel ist vorsätzliche Täuschung
  Reviewed in Germany on October 21, 2014
  Verified Purchase
  Kann ich nur dringend von abraten. Klare vorsätzliche Täuschung der Titel. Es wir einem nicht erklärt was Machine Learning ist, es wird nur R benutzt die Algorithmen zu berechnen oder zu visualisieren und es wird weder R noch maschinelles Lernen erklärt.
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  Kosako2007
  5.0 out of 5 stars Genial introducción al mundo del Machine Learning
  Reviewed in Spain on February 23, 2013
  Verified Purchase
  El libro es una muy buena introducción al Machine Learning y los conceptos en los que se basa. Lo recomiendo para todo aquel desarrollador que quiera introducirse en ese mundo.
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Machine Learning for Hackers: Case Studies and Algorithms to Get You Started: Conway, Drew, White, John Myles: 9781449303716: Amazon.com: Books
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Amazon.com: Machine Learning for Dummies: 9781119245513: Mueller, John Paul, Massaron, Luca: Books
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 Your no-nonsense guide to making sense of machine learning

 Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks.

 Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data&;or anything in between&;this guide makes it easier to understand and implement machine learning seamlessly.

 * Grasp how day-to-day activities are powered by machine learning
 * Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis
 * Learn to code in R using R Studio
 * Find out how to code in Python using Anaconda

 Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!

 Read more
 Report an issue with this product or seller
 Previous page
 1. ISBN-10
 1119245516
 2. ISBN-13
 978-1119245513
 3. Edition
 1st
 4. Publisher
 For Dummies
 5. Publication date
 January 1, 2016
 6. Language
 English
 7. Dimensions
 7.25 x 1 x 9.25 inches
 8. Print length
 410 pages
 9. See all details
 Next page

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 Editorial Reviews

 From the Inside Flap

 * Learn how machine learning algorithms are invaluable
 * Implement machine learning in Python and R
 * Use machine learning to accomplish practical tasks

 Machine learning made easy!

 It may sound a bit intimidating, but machine learning is an exciting new way to teach your computer to perform all sorts of important and useful tasks. This book is the easy way to get up to speed. It explains how to get started, provides detailed discussions of how the underlying algorithms work, uses languages such as Python and R to make machine learning possible, specifies how to do practical things using common algorithms, and more!

 Inside…

 * The real story about AI
 * R and Python coding
 * The role of statistics
 * Managing big data
 * Demystifying the math
 * Using linear models
 * What neural networks do
 * How to work with images
 * More tools you can use

 From the Back Cover

 * Learn how machine learning algorithms are invaluable
 * Implement machine learning in Python and R
 * Use machine learning to accomplish practical tasks

 Machine learning made easy!

 It may sound a bit intimidating, but machine learning is an exciting new way to teach your computer to perform all sorts of important and useful tasks. This book is the easy way to get up to speed. It explains how to get started, provides detailed discussions of how the underlying algorithms work, uses languages such as Python and R to make machine learning possible, specifies how to do practical things using common algorithms, and more!

 Inside&;

 * The real story about AI
 * R and Python coding
 * The role of statistics
 * Managing big data
 * Demystifying the math
 * Using linear models
 * What neural networks do
 * How to work with images
 * More tools you can use
 Read more

 Product details

 * Publisher ‏ : ‎ For Dummies; 1st edition (January 1, 2016)
 * Language ‏ : ‎ English
 * Paperback ‏ : ‎ 410 pages
 * ISBN-10 ‏ : ‎ 1119245516
 * ISBN-13 ‏ : ‎ 978-1119245513
 * Item Weight ‏ : ‎ 1.29 pounds
 * Dimensions ‏ : ‎ 7.25 x 1 x 9.25 inches
 * Best Sellers Rank: #340,858 in Books ( See Top 100 in Books )
 + #586 in Computer Science (Books)
 + #9,046 in Unknown
 * Customer Reviews:
 4.3 4.3 out of 5 stars 504 ratings
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 About the authors

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 Previous page

 1. Luca Massaron

 Brief content visible, double tap to read full content.
 Full content visible, double tap to read brief content.

 Luca Massaron is a data scientist and a research director specialized in multivariate statistical analysis, machine learning and customer insight with over a decade of experience in solving real world problems and in generating value for stakeholders by applying reasoning, statistics, data mining and algorithms. From being a pioneer of Web audience analysis in Italy to achieving the rank of top ten data scientist at competitions held by kaggle.com, he has always been passionate about everything regarding data and analysis and about demonstrating the potentiality of data-driven knowledge discovery to both experts and non-experts. Favouring simplicity over unnecessary sophistication, he believes that a lot can be achieved in data science just by doing the essential.

 See more on the author's page

 2. John Paul Mueller

 Brief content visible, double tap to read full content.
 Full content visible, double tap to read brief content.

 John Mueller is a freelance author and technical editor. He has writing in his blood, having produced 117 books and over 600 articles to date. He has also written his second children's book recently, Tail of the Wuggly Bump. His technical topics range from networking to artificial intelligence and from database management to heads down programming. His most recent book is "Machine Learning for Dummies, 2nd Edition." His technical editing skills have helped over 70 authors refine the content of their manuscripts. You can reach John on the Internet at John@JohnMuellerBooks.com and his Web site at: http://www.johnmuellerbooks.com. Make sure to read his blog at http://blog.johnmuellerbooks.com to obtain the latest book information, updates, and extra materials.

 See more on the author's page
 Next page

  Customer reviews

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  4 Stars
  Physical copy of book was ok (nothing about the content of the book yet)
  Damaged pages as on the pic from pages 209-231. It's readable though.
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  kamarri cummings
  5.0 out of 5 stars Perfect and Useful!
  Reviewed in the United States on April 25, 2019
  Verified Purchase
  This book is awesome because the world of data science and machine learning is complicated enough, this book directs you step by step in layman's terms. Easy to follow and wonderful examples to help you along the way.
  *Thumbs up*
  Read more
  4 people found this helpful
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  CH Richards
  4.0 out of 5 stars it seems like a good place to have started
  Reviewed in the United States on July 30, 2017
  Verified Purchase
  Have not completed this one yet. As a newcomer to the subject, it seems like a good place to have started. Most of the examples seem clear and complete. Sometimes I get confused by the wording, but whether that's issues with syntax and style, or just my ignorance, I can't yet be sure. After I start coding and testing some of the learning programs, I'll have a better perspective.
  Read more
  2 people found this helpful
  Helpful
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  Brock
  5.0 out of 5 stars Counting AI Generated Sheep To Sleep
  Reviewed in the United States on August 13, 2019
  Verified Purchase
  This machine learning book has helped take me from a newb sabot to an elite when it comes to understanding what all of those news articles talking about AI and Machine Learning are really saying. It has been a fascinating and inspiring read, it truly humbles me thinking about how far things have come in technology. I have learned that through math formulas we can mimic some aspects of things only relegated to living breathing creatures in the past.
  Read more
  2 people found this helpful
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  Mary Echternacht
  5.0 out of 5 stars This book is not for dummies
  Reviewed in the United States on March 3, 2017
  Verified Purchase
  This is a very good place to begin a journey into the very interesting and more and more important topic of this era, examples are showing up all around us, we are using this technology to aid us in our performance, but do not let the title fool you, as you well know, you will be confronted with some very powerful and complex thinking, but again as the title implies, you will walk away with a good comprehension of this ever so more important field
  Read more
  9 people found this helpful
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  Daniel Sionov
  3.0 out of 5 stars Tedious
  Reviewed in the United States on August 9, 2016
  Verified Purchase
  Not very interesting. Very tedious.
  Read more
  One person found this helpful
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  Artem Naumov
  5.0 out of 5 stars The Dummies are the best technical series saving any professional tons of teaching time, also improving newbie's confidence
  Reviewed in the United States on August 11, 2016
  Verified Purchase
  The "Dummies" always exceed your expectations by not only explaining complex things in a simple way, but also by giving you extra details and sufficient range of professional real-world issues that will actually make you look professional on a new topic in a typical industry-pro conversation and even a job interview. Love to recommend those books and sometimes read them myself as it is the fastest way to get a proper introduction and directions for further study.
  Read more
  3 people found this helpful
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  Jeff Brown
  4.0 out of 5 stars Good book, but as it progressed into the meatier ...
  Reviewed in the United States on March 3, 2017
  Verified Purchase
  Good book, but as it progressed into the meatier sections of ML, I thought they moved away from the "for dummies" style of trying to explain things in layman's term and using examples that are simple and help you understand the concepts and intuitions of the material.
  Read more
  8 people found this helpful
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  Kenneth E. Mayer
  5.0 out of 5 stars Great overall coverage of machine learning
  Reviewed in the United States on March 12, 2019
  Verified Purchase
  This book helped give me a solid overview of machine learning in Python. It covers all of the bases, from setting up Python on the computer to the different machine learning algorithms. Also, there is code available online so you can bring it into your IDE.
  Read more
  4 people found this helpful
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  Gabriel Quintanar
  5.0 out of 5 stars Completo y fácil de digerir
  Reviewed in Mexico on September 27, 2018
  Verified Purchase
  Usualmente me gusta tener un "...for dummies" cuando quiero estudiar por las distintas notas y lo fácil que es de digerir. Este no es una excepción. Muy buen libro introductorio.
  Read more
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  Teodora
  5.0 out of 5 stars Intro to ML
  Reviewed in Canada on August 10, 2018
  Verified Purchase
  Great for absolute beginners, or those who have or want to understand the Machine Learning space without necessarily doing direct work in the field. This book provides a hood introduction and overview of the topic.
  Read more
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  ali
  5.0 out of 5 stars Attention to detail
  Reviewed in the United Kingdom on December 26, 2019
  Verified Purchase
  Bought that as a new learning tool. It does the job. Good book.
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  Hh
  5.0 out of 5 stars Disappointed
  Reviewed in Germany on November 26, 2019
  Verified Purchase
  Very detailed.
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  Dr. Manas S
  5.0 out of 5 stars Good beginners' book that quickly gets you started hands-on
  Reviewed in India on June 13, 2019
  Verified Purchase
  I have read theoretical books on AI/ML; they do explain concepts but do not make you get started hands-on. This book does that. It has a few foundational chapters to cover the basics and then applies them either in R or Python. Note that you cannot be too naive in maths or understanding program flows if you intend to work in the area of AI/ML. Hence, it is good to have supplementary books on maths (basic statistics, matrix and probability) and general programming to quickly refer back. When I bought the book, thought that it is good to know two languages as well; but while reading it, felt a bit uncomfortable while switching from one example in one language to the other in another language; however, examples themselves are self-sufficient and comprehensible.
  Read more
  18 people found this helpful
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 Machine Learning for Dummies 1st Edition
 by John Paul Mueller (Author), Luca Massaron (Author)
 4.3 4.3 out of 5 stars 504 ratings
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 There is a newer edition of this item:
 Machine Learning For Dummies
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 Your no-nonsense guide to making sense of machine learning
 Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks.
 Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data&;or anything in between&;this guide makes it easier to understand and implement machine learning seamlessly.
 * Grasp how day-to-day activities are powered by machine learning
 * Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis
 * Learn to code in R using R Studio
 * Find out how to code in Python using Anaconda
 Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!
 Read more
 Report an issue with this product or seller
 Previous page
 1. ISBN-10
 1119245516
 2. ISBN-13
 978-1119245513
 3. Edition
 1st
 4. Publisher
 For Dummies
 5. Publication date
 January 1, 2016
 6. Language
 English
 7. Dimensions
 7.25 x 1 x 9.25 inches
 8. Print length
 410 pages
 9. See all details
 Next page
 Similar items that may deliver to you quickly
 Page 1 of 1 Start over Page 1 of 1
 Previous page
 1. Machine Learning For Dummies
 John Paul Mueller
 4.4 out of 5 stars 93
 Paperback
 $20.49 $ 20 . 49
 Get it as soon as Monday, May 6
 FREE Shipping on orders over $35 shipped by Amazon
 2. Artificial Intelligence For Dummies, 2nd Edition (For Dummies (Computer/Tech))
 John Paul Mueller
 4.1 out of 5 stars 95
 Paperback
 $23.78 $ 23 . 78
 Get it as soon as Monday, May 6
 FREE Shipping on orders over $35 shipped by Amazon
 3. Machine Learning for Absolute Beginners: A Plain English Introduction (Third Edition) (Machine Learning with Python for Beginners Book Series)
 Oliver Theobald
 4.4 out of 5 stars 340
 Paperback
 $16.57 $ 16 . 57
 Get it as soon as Monday, May 6
 FREE Shipping on orders over $35 shipped by Amazon
 4. Machine Learning For Absolute Beginners: A Plain English Introduction (AI, Data Science, Python & Statistics for Beginners)
 Oliver Theobald
 4.4 out of 5 stars 1,538
 Paperback
 $16.00 $ 16 . 00
 Get it as soon as Monday, May 6
 FREE Shipping on orders over $35 shipped by Amazon
 5. The Hundred-Page Machine Learning Book
 Andriy Burkov
 4.6 out of 5 stars 1,237
 Paperback
 $30.51 $ 30 . 51
 Get it as soon as Monday, May 6
 FREE Shipping on orders over $35 shipped by Amazon
 6. Neural Networks and Deep Learning: A Textbook
 Charu C. Aggarwal
 4.4 out of 5 stars 24
 Hardcover
 $51.77 $ 51 . 77
 Get it as soon as Monday, May 6
 FREE Shipping by Amazon
 Next page
 Editorial Reviews
 From the Inside Flap
 * Learn how machine learning algorithms are invaluable
 * Implement machine learning in Python and R
 * Use machine learning to accomplish practical tasks
 Machine learning made easy!
 It may sound a bit intimidating, but machine learning is an exciting new way to teach your computer to perform all sorts of important and useful tasks. This book is the easy way to get up to speed. It explains how to get started, provides detailed discussions of how the underlying algorithms work, uses languages such as Python and R to make machine learning possible, specifies how to do practical things using common algorithms, and more!
 Inside…
 * The real story about AI
 * R and Python coding
 * The role of statistics
 * Managing big data
 * Demystifying the math
 * Using linear models
 * What neural networks do
 * How to work with images
 * More tools you can use
 From the Back Cover
 * Learn how machine learning algorithms are invaluable
 * Implement machine learning in Python and R
 * Use machine learning to accomplish practical tasks
 Machine learning made easy!
 It may sound a bit intimidating, but machine learning is an exciting new way to teach your computer to perform all sorts of important and useful tasks. This book is the easy way to get up to speed. It explains how to get started, provides detailed discussions of how the underlying algorithms work, uses languages such as Python and R to make machine learning possible, specifies how to do practical things using common algorithms, and more!
 Inside&;
 * The real story about AI
 * R and Python coding
 * The role of statistics
 * Managing big data
 * Demystifying the math
 * Using linear models
 * What neural networks do
 * How to work with images
 * More tools you can use
 Read more
 Product details
 * Publisher ‏ : ‎ For Dummies; 1st edition (January 1, 2016)
 * Language ‏ : ‎ English
 * Paperback ‏ : ‎ 410 pages
 * ISBN-10 ‏ : ‎ 1119245516
 * ISBN-13 ‏ : ‎ 978-1119245513
 * Item Weight ‏ : ‎ 1.29 pounds
 * Dimensions ‏ : ‎ 7.25 x 1 x 9.25 inches
 * Best Sellers Rank: #340,858 in Books ( See Top 100 in Books )
 + #586 in Computer Science (Books)
 + #9,046 in Unknown
 * Customer Reviews:
 4.3 4.3 out of 5 stars 504 ratings
 Brief content visible, double tap to read full content.
 Full content visible, double tap to read brief content.
 Videos
 Help others learn more about this product by uploading a video!
 Upload your video
 About the authors
 Follow authors to get new release updates, plus improved recommendations.
 Previous page
 1. Luca Massaron
 Brief content visible, double tap to read full content.
 Full content visible, double tap to read brief content.
 Luca Massaron is a data scientist and a research director specialized in multivariate statistical analysis, machine learning and customer insight with over a decade of experience in solving real world problems and in generating value for stakeholders by applying reasoning, statistics, data mining and algorithms. From being a pioneer of Web audience analysis in Italy to achieving the rank of top ten data scientist at competitions held by kaggle.com, he has always been passionate about everything regarding data and analysis and about demonstrating the potentiality of data-driven knowledge discovery to both experts and non-experts. Favouring simplicity over unnecessary sophistication, he believes that a lot can be achieved in data science just by doing the essential.
 See more on the author's page
 2. John Paul Mueller
 Brief content visible, double tap to read full content.
 Full content visible, double tap to read brief content.
 John Mueller is a freelance author and technical editor. He has writing in his blood, having produced 117 books and over 600 articles to date. He has also written his second children's book recently, Tail of the Wuggly Bump. His technical topics range from networking to artificial intelligence and from database management to heads down programming. His most recent book is "Machine Learning for Dummies, 2nd Edition." His technical editing skills have helped over 70 authors refine the content of their manuscripts. You can reach John on the Internet at John@JohnMuellerBooks.com and his Web site at: http://www.johnmuellerbooks.com. Make sure to read his blog at http://blog.johnmuellerbooks.com to obtain the latest book information, updates, and extra materials.
 See more on the author's page
 Next page
  Customer reviews
  4.3 out of 5 stars
  4.3 out of 5
  504 global ratings
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  4 star 22%
  3 star 11%
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  4 Stars
  Physical copy of book was ok (nothing about the content of the book yet)
  Damaged pages as on the pic from pages 209-231. It's readable though.
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  kamarri cummings
  5.0 out of 5 stars Perfect and Useful!
  Reviewed in the United States on April 25, 2019
  Verified Purchase
  This book is awesome because the world of data science and machine learning is complicated enough, this book directs you step by step in layman's terms. Easy to follow and wonderful examples to help you along the way.
  *Thumbs up*
  Read more
  4 people found this helpful
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  CH Richards
  4.0 out of 5 stars it seems like a good place to have started
  Reviewed in the United States on July 30, 2017
  Verified Purchase
  Have not completed this one yet. As a newcomer to the subject, it seems like a good place to have started. Most of the examples seem clear and complete. Sometimes I get confused by the wording, but whether that's issues with syntax and style, or just my ignorance, I can't yet be sure. After I start coding and testing some of the learning programs, I'll have a better perspective.
  Read more
  2 people found this helpful
  Helpful
  Report
  Brock
  5.0 out of 5 stars Counting AI Generated Sheep To Sleep
  Reviewed in the United States on August 13, 2019
  Verified Purchase
  This machine learning book has helped take me from a newb sabot to an elite when it comes to understanding what all of those news articles talking about AI and Machine Learning are really saying. It has been a fascinating and inspiring read, it truly humbles me thinking about how far things have come in technology. I have learned that through math formulas we can mimic some aspects of things only relegated to living breathing creatures in the past.
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  Mary Echternacht
  5.0 out of 5 stars This book is not for dummies
  Reviewed in the United States on March 3, 2017
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  This is a very good place to begin a journey into the very interesting and more and more important topic of this era, examples are showing up all around us, we are using this technology to aid us in our performance, but do not let the title fool you, as you well know, you will be confronted with some very powerful and complex thinking, but again as the title implies, you will walk away with a good comprehension of this ever so more important field
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  Daniel Sionov
  3.0 out of 5 stars Tedious
  Reviewed in the United States on August 9, 2016
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  Not very interesting. Very tedious.
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  Artem Naumov
  5.0 out of 5 stars The Dummies are the best technical series saving any professional tons of teaching time, also improving newbie's confidence
  Reviewed in the United States on August 11, 2016
  Verified Purchase
  The "Dummies" always exceed your expectations by not only explaining complex things in a simple way, but also by giving you extra details and sufficient range of professional real-world issues that will actually make you look professional on a new topic in a typical industry-pro conversation and even a job interview. Love to recommend those books and sometimes read them myself as it is the fastest way to get a proper introduction and directions for further study.
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  Jeff Brown
  4.0 out of 5 stars Good book, but as it progressed into the meatier ...
  Reviewed in the United States on March 3, 2017
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  Good book, but as it progressed into the meatier sections of ML, I thought they moved away from the "for dummies" style of trying to explain things in layman's term and using examples that are simple and help you understand the concepts and intuitions of the material.
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  Kenneth E. Mayer
  5.0 out of 5 stars Great overall coverage of machine learning
  Reviewed in the United States on March 12, 2019
  Verified Purchase
  This book helped give me a solid overview of machine learning in Python. It covers all of the bases, from setting up Python on the computer to the different machine learning algorithms. Also, there is code available online so you can bring it into your IDE.
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  Gabriel Quintanar
  5.0 out of 5 stars Completo y fácil de digerir
  Reviewed in Mexico on September 27, 2018
  Verified Purchase
  Usualmente me gusta tener un "...for dummies" cuando quiero estudiar por las distintas notas y lo fácil que es de digerir. Este no es una excepción. Muy buen libro introductorio.
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  Teodora
  5.0 out of 5 stars Intro to ML
  Reviewed in Canada on August 10, 2018
  Verified Purchase
  Great for absolute beginners, or those who have or want to understand the Machine Learning space without necessarily doing direct work in the field. This book provides a hood introduction and overview of the topic.
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  ali
  5.0 out of 5 stars Attention to detail
  Reviewed in the United Kingdom on December 26, 2019
  Verified Purchase
  Bought that as a new learning tool. It does the job. Good book.
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  Hh
  5.0 out of 5 stars Disappointed
  Reviewed in Germany on November 26, 2019
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  Very detailed.
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  Dr. Manas S
  5.0 out of 5 stars Good beginners' book that quickly gets you started hands-on
  Reviewed in India on June 13, 2019
  Verified Purchase
  I have read theoretical books on AI/ML; they do explain concepts but do not make you get started hands-on. This book does that. It has a few foundational chapters to cover the basics and then applies them either in R or Python. Note that you cannot be too naive in maths or understanding program flows if you intend to work in the area of AI/ML. Hence, it is good to have supplementary books on maths (basic statistics, matrix and probability) and general programming to quickly refer back. When I bought the book, thought that it is good to know two languages as well; but while reading it, felt a bit uncomfortable while switching from one example in one language to the other in another language; however, examples themselves are self-sufficient and comprehensible.
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Amazon.com: Machine Learning for Dummies: 9781119245513: Mueller, John Paul, Massaron, Luca: Books
https://www.amazon.com/Machine-Learning-Dummies-John-Mueller/dp/1119245516?tag=hackr-20&geniuslink=true
https://www.amazon.com/Machine-Learning-Absolute-Beginners-Introduction-ebook/dp/B06VXKBLNG?tag=hackr-20&geniuslink=true
Amazon.com: Machine Learning for Absolute Beginners: A Plain English Introduction (First Edition) eBook : Theobald, Oliver: Kindle Store
2017-02-18
Oliver Theobald
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 Machine Learning for Absolute Beginners: A Plain English Introduction (First Edition) Kindle Edition

 by Oliver Theobald (Author) Format: Kindle Edition
 4.2 4.2 out of 5 stars 488 ratings
 
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 Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?

 Well, hold on there...

 Before you embark on your epic journey into the world of machine learning, there is some basic theory to march through first.
 However, rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first. As a clear and concise alternative to a textbook, this short book offers a practical and high-level introduction to the practical components and statistical concepts found in machine learning.

 Machine Learning for Absolute Beginners has been written and designed for absolute beginners . This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home.

 This title opens with a general introduction to machine learning from a macro level. The second half of the book is more practical and dives into introducing statistical concepts and specific algorithms applied in machine learning, including their pros and cons. At the end of the book, I share insights and advice on further learning and careers in this space.

 Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle deep learning and Scikit-learn, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment - as a fully grown Simba looking over the Pride Lands of Africa - then this is the book to gently hoist you up and offer you a clear lay of the land.

 In this step-by-step guide you will learn:
 • The very basics of Machine Learning that all beginners need to master
 • Decision Trees for visually mapping and classifying decision processes
 • Regression Analysis to create trend lines and predict trends
 • Data Reduction to cut through the noise
 • k-means and k-nearest Neighbor (k-nn) Clustering to discover new data groups
 • Introduction to Deep Learning/Neural Networks
 • Bias/Variance to optimize your machine learning model
 • Create your first machine learning model to predict video game sales using Python
 • Careers in the field

 Please also note that under Amazon’s Kindle Book Lending program, you can lend this e-book to friends and family for a duration of 14 days (https://www.amazon.com/gp/help/customer/display.html).

 If you want to learn more, please go ahead and send a free sample to your device or check out Amazon's handy 'Look Inside' feature.

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 Product details

 * ASIN ‏ : ‎ B06VXKBLNG
 * Publisher ‏ : ‎ Scatterplot Press; 1st edition (February 18, 2017)
 * Publication date ‏ : ‎ February 18, 2017
 * Language ‏ : ‎ English
 * File size ‏ : ‎ 8567 KB
 * Text-to-Speech ‏ : ‎ Enabled
 * Screen Reader ‏ : ‎ Supported
 * Enhanced typesetting ‏ : ‎ Enabled
 * X-Ray ‏ : ‎ Enabled
 * Word Wise ‏ : ‎ Not Enabled
 * Sticky notes ‏ : ‎ On Kindle Scribe
 * Print length ‏ : ‎ 169 pages
 * Best Sellers Rank: #978,266 in Kindle Store ( See Top 100 in Kindle Store )
 + #96 in Machine Theory (Kindle Store)
 + #251 in Machine Theory (Books)
 + #276 in Data Modeling & Design (Kindle Store)
 * Customer Reviews:
 4.2 4.2 out of 5 stars 488 ratings
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  Devendra Patel
  5.0 out of 5 stars Machine Learning at a Glance
  Reviewed in the United States on December 27, 2023
  Verified Purchase
  Covers mostly everything you need to know about Machine Learning at 30k feet level. It makes you more interested in learning more about it.
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  Dr. Nesr
  4.0 out of 5 stars Good for beginners, but!
  Reviewed in the United States on November 22, 2020
  Verified Purchase
  The book is just as described, for absolute beginners, I think it has a bit deeper vision or some examples, quizzes, or exercises to help the reader reinforce the idea.
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  simone smith
  5.0 out of 5 stars Got more than my moneys worth!
  Reviewed in the United States on September 15, 2017
  Verified Purchase
  I purchased two introductory e-books on Machine Learning. One cost me $20 and this one cost me $3. The $20 book had 399 pages but I got so completely lost before hitting page 100 that I gave up in defeat. (My iPad Kindle app doesn’t have page numbers, so I can’t tell you exactly what page I was on but it seemed like I had been reading forever).

  Then I found this $3 book – Machine Learning for Absolute Beginners – was not only short but it came out as the clear winner because I was able to get through right to the end of it with ease and without confusion. So turns out I got more than my money’s worth here with this book!

  And on top of that, this book was clear, well-written and a definite confidence builder. The knowledge I’ve gained from this book regarding ML models and basic algorithms gives me more confidence to go back to the other more expensive book for a second attempt.

  In summary, if this is your first foray into Machine Learning then this here is your book! It is the perfect stepping stone to other more lengthy options.
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  Joseph F Petrie Jr
  3.0 out of 5 stars Machine Learning title is misleading
  Reviewed in the United States on July 6, 2017
  Verified Purchase
  This book's name is misleading. About 50% of the book is dedicated to reiterating the concepts that you will have learned in classical statistics. While I found this greatly reassuring there is very little information about the actual process of machine learning and more information about the salaries, classes offered, and degree programs you can enlist in.

  While I did learn a couple of valuable things, I would not recommend this book to anyone who has taken statistics or understands the basic concept of how SEO and algorithms work. It doesn't go in depth into how machine learning works.

  I would recommend this to people who have not taken statistics before although you may also need to read another book specifically on statistics to get the concept
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  Carlos O. Pabon
  4.0 out of 5 stars Short but worth it
  Reviewed in the United States on July 7, 2017
  Verified Purchase
  The book is short but I think that's it's strong suit. I suspect it's usefulness will wear off as this information becomes more foundational knowledge through my research into ML but as a beginner in the field, I find that having an easy to navigate reference book for the algorithms I need to learn to become an ML expert is very useful.
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  Ghia Smith
  5.0 out of 5 stars Short but sweet
  Reviewed in the United States on September 18, 2017
  Verified Purchase
  This book lives up to its name as a well-put together guide for ABSOLUTE BEGINNERS. The author handles the subject with brevity but clear prose.

  It answered a lot of my questions and the blank spots I had re: what’s the difference between ML and standard computing programming and how ML works in terms of statistical modelling.

  The book doesn’t go too deep into any particular part of ML but the chapter on ANN was particularly insightful in tackling a fairly challenging subject within ML.

  As a standalone book it won’t be enough to train yourself to become a machine learning engineer – although it does include a chapter on job & study options – so it really should be used as an entry point to ML and not a comprehensive guide. To be honest, I’d still be CLUELESS if I went to my CLI today and wanted to start a machine learning model. But for a few bucks this book has done its job and I feel much more confident tackling other introductory resources to Scikit Learn and Tensorflow now that I have all the basic principles and terminology fresh in my head.
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  fitzalling
  5.0 out of 5 stars If you are interested in a data science/machine learning, read this book
  Reviewed in the United States on March 27, 2017
  Verified Purchase
  The book is short and moves along fairly well. What do you get? The author gives an overview of data science that distinguishes machine learning from data mining, artificial intelligence, etc. I found this useful. He gives specific examples of how machine learning is implemented in marketing and elsewhere. Different types of algorithms, some supported by humans and some not, do the machine learning or data mining or other data science. The author does not go into great depth on these algorithms, which is consistent with his goal of providing an overview for beginners.

  The book has some math. Once again, I think this is included to alert a beginner that understanding and working with math is mandatory to work in this field. The author lists job titles and salary ranges for jobs in the data science/machine learning fields.

  If you or someone you know think that data science may be a career in which you would be interested, I recommend the book.
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  Ruben D. Trevino Gonzalez
  4.0 out of 5 stars What I expected
  Reviewed in the United States on September 16, 2017
  Verified Purchase
  Great introduction to machine learning, which is a term that gets confused a great deal.

  Clear and simple, in my case, it's all I needed, but it can get you started, if you want to go deeper.
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  dsnehash
  5.0 out of 5 stars Great book for beginners
  Reviewed in Canada on April 3, 2023
  Verified Purchase
  Written in a very simple and easy to understand way, this book does a great job in explaining key concepts of machine learning for people starting to explore this vast area. I am so glad I got my hand on this book and read it.
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  Paul
  5.0 out of 5 stars EXCLLENT
  Reviewed in France on February 12, 2020
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  Fabricio Salinas
  4.0 out of 5 stars Buen punto de entrada
  Reviewed in Mexico on September 1, 2017
  Verified Purchase
  Es un libro que explica lo necesario para adentrarse en el maravilloso mundo de Machine Learning. Altamente recomendable para aquellos que quieran iniciar y desconozcan los conceptos básicos.
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  Ralf Jeffery
  5.0 out of 5 stars Excellent primer
  Reviewed in the United Kingdom on December 23, 2017
  Verified Purchase
  As someone who left school with no more than a maths O level, I have been looking for something to explain the basis of ML without delving into complex maths or expecting the reader to be a computer science grad.
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  Nitish Kulkarni
  5.0 out of 5 stars Nice book for those, who has just started gaining knowledge in machine learning! Also some guidelines are given to exactly go where and refer what in future at the end of the book, which I found very helpful!
  Reviewed in Germany on July 26, 2018
  Verified Purchase
  Nice book for those, who has just started gaining knowledge in machine learning! Also some guidelines are given to exactly go where and refer what in future at the end of the book, which I found very helpful!
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 Machine Learning for Absolute Beginners: A Plain English Introduction (First Edition) Kindle Edition
 by Oliver Theobald (Author) Format: Kindle Edition
 4.2 4.2 out of 5 stars 488 ratings
 
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 Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?
 Well, hold on there...
 Before you embark on your epic journey into the world of machine learning, there is some basic theory to march through first.
 However, rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first. As a clear and concise alternative to a textbook, this short book offers a practical and high-level introduction to the practical components and statistical concepts found in machine learning.
 Machine Learning for Absolute Beginners has been written and designed for absolute beginners . This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home.
 This title opens with a general introduction to machine learning from a macro level. The second half of the book is more practical and dives into introducing statistical concepts and specific algorithms applied in machine learning, including their pros and cons. At the end of the book, I share insights and advice on further learning and careers in this space.
 Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle deep learning and Scikit-learn, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment - as a fully grown Simba looking over the Pride Lands of Africa - then this is the book to gently hoist you up and offer you a clear lay of the land.
 In this step-by-step guide you will learn:
 • The very basics of Machine Learning that all beginners need to master
 • Decision Trees for visually mapping and classifying decision processes
 • Regression Analysis to create trend lines and predict trends
 • Data Reduction to cut through the noise
 • k-means and k-nearest Neighbor (k-nn) Clustering to discover new data groups
 • Introduction to Deep Learning/Neural Networks
 • Bias/Variance to optimize your machine learning model
 • Create your first machine learning model to predict video game sales using Python
 • Careers in the field
 Please also note that under Amazon’s Kindle Book Lending program, you can lend this e-book to friends and family for a duration of 14 days (https://www.amazon.com/gp/help/customer/display.html).
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 * ASIN ‏ : ‎ B06VXKBLNG
 * Publisher ‏ : ‎ Scatterplot Press; 1st edition (February 18, 2017)
 * Publication date ‏ : ‎ February 18, 2017
 * Language ‏ : ‎ English
 * File size ‏ : ‎ 8567 KB
 * Text-to-Speech ‏ : ‎ Enabled
 * Screen Reader ‏ : ‎ Supported
 * Enhanced typesetting ‏ : ‎ Enabled
 * X-Ray ‏ : ‎ Enabled
 * Word Wise ‏ : ‎ Not Enabled
 * Sticky notes ‏ : ‎ On Kindle Scribe
 * Print length ‏ : ‎ 169 pages
 * Best Sellers Rank: #978,266 in Kindle Store ( See Top 100 in Kindle Store )
 + #96 in Machine Theory (Kindle Store)
 + #251 in Machine Theory (Books)
 + #276 in Data Modeling & Design (Kindle Store)
 * Customer Reviews:
 4.2 4.2 out of 5 stars 488 ratings
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  4.2 out of 5 stars
  4.2 out of 5
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  Devendra Patel
  5.0 out of 5 stars Machine Learning at a Glance
  Reviewed in the United States on December 27, 2023
  Verified Purchase
  Covers mostly everything you need to know about Machine Learning at 30k feet level. It makes you more interested in learning more about it.
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  Dr. Nesr
  4.0 out of 5 stars Good for beginners, but!
  Reviewed in the United States on November 22, 2020
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  The book is just as described, for absolute beginners, I think it has a bit deeper vision or some examples, quizzes, or exercises to help the reader reinforce the idea.
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  simone smith
  5.0 out of 5 stars Got more than my moneys worth!
  Reviewed in the United States on September 15, 2017
  Verified Purchase
  I purchased two introductory e-books on Machine Learning. One cost me $20 and this one cost me $3. The $20 book had 399 pages but I got so completely lost before hitting page 100 that I gave up in defeat. (My iPad Kindle app doesn’t have page numbers, so I can’t tell you exactly what page I was on but it seemed like I had been reading forever).
  Then I found this $3 book – Machine Learning for Absolute Beginners – was not only short but it came out as the clear winner because I was able to get through right to the end of it with ease and without confusion. So turns out I got more than my money’s worth here with this book!
  And on top of that, this book was clear, well-written and a definite confidence builder. The knowledge I’ve gained from this book regarding ML models and basic algorithms gives me more confidence to go back to the other more expensive book for a second attempt.
  In summary, if this is your first foray into Machine Learning then this here is your book! It is the perfect stepping stone to other more lengthy options.
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  6 people found this helpful
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  Joseph F Petrie Jr
  3.0 out of 5 stars Machine Learning title is misleading
  Reviewed in the United States on July 6, 2017
  Verified Purchase
  This book's name is misleading. About 50% of the book is dedicated to reiterating the concepts that you will have learned in classical statistics. While I found this greatly reassuring there is very little information about the actual process of machine learning and more information about the salaries, classes offered, and degree programs you can enlist in.
  While I did learn a couple of valuable things, I would not recommend this book to anyone who has taken statistics or understands the basic concept of how SEO and algorithms work. It doesn't go in depth into how machine learning works.
  I would recommend this to people who have not taken statistics before although you may also need to read another book specifically on statistics to get the concept
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  9 people found this helpful
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  Carlos O. Pabon
  4.0 out of 5 stars Short but worth it
  Reviewed in the United States on July 7, 2017
  Verified Purchase
  The book is short but I think that's it's strong suit. I suspect it's usefulness will wear off as this information becomes more foundational knowledge through my research into ML but as a beginner in the field, I find that having an easy to navigate reference book for the algorithms I need to learn to become an ML expert is very useful.
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  Ghia Smith
  5.0 out of 5 stars Short but sweet
  Reviewed in the United States on September 18, 2017
  Verified Purchase
  This book lives up to its name as a well-put together guide for ABSOLUTE BEGINNERS. The author handles the subject with brevity but clear prose.
  It answered a lot of my questions and the blank spots I had re: what’s the difference between ML and standard computing programming and how ML works in terms of statistical modelling.
  The book doesn’t go too deep into any particular part of ML but the chapter on ANN was particularly insightful in tackling a fairly challenging subject within ML.
  As a standalone book it won’t be enough to train yourself to become a machine learning engineer – although it does include a chapter on job & study options – so it really should be used as an entry point to ML and not a comprehensive guide. To be honest, I’d still be CLUELESS if I went to my CLI today and wanted to start a machine learning model. But for a few bucks this book has done its job and I feel much more confident tackling other introductory resources to Scikit Learn and Tensorflow now that I have all the basic principles and terminology fresh in my head.
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  fitzalling
  5.0 out of 5 stars If you are interested in a data science/machine learning, read this book
  Reviewed in the United States on March 27, 2017
  Verified Purchase
  The book is short and moves along fairly well. What do you get? The author gives an overview of data science that distinguishes machine learning from data mining, artificial intelligence, etc. I found this useful. He gives specific examples of how machine learning is implemented in marketing and elsewhere. Different types of algorithms, some supported by humans and some not, do the machine learning or data mining or other data science. The author does not go into great depth on these algorithms, which is consistent with his goal of providing an overview for beginners.
  The book has some math. Once again, I think this is included to alert a beginner that understanding and working with math is mandatory to work in this field. The author lists job titles and salary ranges for jobs in the data science/machine learning fields.
  If you or someone you know think that data science may be a career in which you would be interested, I recommend the book.
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  22 people found this helpful
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  Ruben D. Trevino Gonzalez
  4.0 out of 5 stars What I expected
  Reviewed in the United States on September 16, 2017
  Verified Purchase
  Great introduction to machine learning, which is a term that gets confused a great deal.
  Clear and simple, in my case, it's all I needed, but it can get you started, if you want to go deeper.
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  dsnehash
  5.0 out of 5 stars Great book for beginners
  Reviewed in Canada on April 3, 2023
  Verified Purchase
  Written in a very simple and easy to understand way, this book does a great job in explaining key concepts of machine learning for people starting to explore this vast area. I am so glad I got my hand on this book and read it.
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  Paul
  5.0 out of 5 stars EXCLLENT
  Reviewed in France on February 12, 2020
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  Fabricio Salinas
  4.0 out of 5 stars Buen punto de entrada
  Reviewed in Mexico on September 1, 2017
  Verified Purchase
  Es un libro que explica lo necesario para adentrarse en el maravilloso mundo de Machine Learning. Altamente recomendable para aquellos que quieran iniciar y desconozcan los conceptos básicos.
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  Ralf Jeffery
  5.0 out of 5 stars Excellent primer
  Reviewed in the United Kingdom on December 23, 2017
  Verified Purchase
  As someone who left school with no more than a maths O level, I have been looking for something to explain the basis of ML without delving into complex maths or expecting the reader to be a computer science grad.
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  Nitish Kulkarni
  5.0 out of 5 stars Nice book for those, who has just started gaining knowledge in machine learning! Also some guidelines are given to exactly go where and refer what in future at the end of the book, which I found very helpful!
  Reviewed in Germany on July 26, 2018
  Verified Purchase
  Nice book for those, who has just started gaining knowledge in machine learning! Also some guidelines are given to exactly go where and refer what in future at the end of the book, which I found very helpful!
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Machine Learning for Hackers: Case Studies and Algorithms to Get You Started: Conway, Drew, White, John Myles: 9781449303716: Amazon.com: Books
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 Machine Learning for Hackers: Case Studies and Algorithms to Get You Started First Edition

 by Drew Conway (Author), John Myles White
 3.7 3.7 out of 5 stars 62 ratings
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 If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning―a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.

 Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.

 * Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text
 * Use linear regression to predict the number of page views for the top 1,000 websites
 * Learn optimization techniques by attempting to break a simple letter cipher
 * Compare and contrast U.S. Senators statistically, based on their voting records
 * Build a “whom to follow” recommendation system from Twitter data
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 1. ISBN-10
 1449303714
 2. ISBN-13
 978-1449303716
 3. Edition
 First Edition
 4. Publisher
 O'Reilly
 5. Publication date
 March 20, 2012
 6. Language
 English
 7. Dimensions
 7 x 0.75 x 9.19 inches
 8. Print length
 320 pages
 9. See all details
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 Editorial Reviews

 About the Author

 Drew Conway is a PhD candidate in Politics at NYU. He studies international relations, conflict, and terrorism using the tools of mathematics, statistics, and computer science in an attempt to gain a deeper understanding of these phenomena. His academic curiosity is informed by his years as an analyst in the U.S. intelligence and defense communities.

 John Myles White is a PhD candidate in Psychology at Princeton. He studies pattern recognition, decision-making, and economic behavior using behavioral methods and fMRI. He is particularly interested in anomalies of value assessment.

 Product details

 * Publisher ‏ : ‎ O'Reilly; First Edition (March 20, 2012)
 * Language ‏ : ‎ English
 * Paperback ‏ : ‎ 320 pages
 * ISBN-10 ‏ : ‎ 1449303714
 * ISBN-13 ‏ : ‎ 978-1449303716
 * Item Weight ‏ : ‎ 1.14 pounds
 * Dimensions ‏ : ‎ 7 x 0.75 x 9.19 inches
 * Best Sellers Rank: #1,114,970 in Books ( See Top 100 in Books )
 + #208 in Machine Theory (Books)
 + #375 in Natural Language Processing (Books)
 + #740 in Computer Hacking
 * Customer Reviews:
 3.7 3.7 out of 5 stars 62 ratings
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  3.7 out of 5 stars
  3.7 out of 5
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  4 star 16%
  3 star 15%
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  W. Hogan
  5.0 out of 5 stars An interesting and easy read
  Reviewed in the United States on December 12, 2013
  Verified Purchase
  I enjoyed reading this book.

  Pro's:
  The text is parsimonious.
  The examples are interesting.
  The coding is clever.
  The book is less expensive and easier to understand than most Springer texts.

  Con's:
  A substantial part of the code is peripheral tasks; this can be skipped.
  Some of the code is out of date.

  These Con's are trivial. The book is great. I would buy any other books written by these authors.
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  2 people found this helpful
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  Graham
  2.0 out of 5 stars Entry Level R Programming Book
  Reviewed in the United States on November 13, 2016
  Verified Purchase
  This text offers a detailed description in each of 10 case studies about how to build a machine learning solution to the particular problem mentioned. The authors do this in R, and are extensively descriptive about the mechanics of writing R code. If you've never written a computer program, but want to understand how to implement a prewritten machine learning tool in R, this book could be of assistance. However, I'm not sure the authors actually understand the mathematical theory of machine learning; in this book they constantly substitute descriptions of how to select appropriate algorithm parameters with a trial-and-error approach, they do not explain how the algorithms work, and I'm not sure they ever mention the mechanics of "learning" with respect to mathematics. The book has brief passages about machine learning hidden amongst vast chapters about how to read computer directories and load data, etc. Again this book could be useful to the reader who understands ML, but not computer programming.
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  4 people found this helpful
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  Chris
  4.0 out of 5 stars Learned something new
  Reviewed in the United States on October 2, 2012
  Verified Purchase
  I learned a little about the R language and how a lot of scientist of physicist use it the write functions to dynamically create graphs to analyze the data they compiled. It also encompasses a lot of the same math you learn in statistics classes which makes sense. I even ended up downloading an R language reference app from the Apple app store to have an object reference of the language.
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  Voracious Reader
  3.0 out of 5 stars Machine Learning for Non-Hackers
  Reviewed in the United States on March 21, 2012
  Verified Purchase
  By page count, this is primarily a book on R, with some additional time spent on machine learning.

  There is way too much time spent on R, dedicated to such things as parsing email messages, and spidering webpages, etc. These are things that no-one with other tools available would do in R. And it's not that it's easier to do it in R, it's actually harder than using an appropriate library, like JavaMail. And yet, while much time is spent in details, like regexes to extract dates (ick!), more interesting R functions are given short shrift.

  There's some good material in here, but it's buried under the weight of doing everything in R. If you are a non-programmer, and want to use only one hammer for everything, then R is not a bad choice. But it's not a good choice for developers that are already comfortable with a wider variety of tools.

  I'd recommend Programming Collective Intelligence by Segaran, if you would describe yourself as a "Hacker".
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  145 people found this helpful
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  Ravi Aranke
  5.0 out of 5 stars Excellent and immediately practical, if you already know some R
  Reviewed in the United States on February 20, 2012
  I started my journey in the machine learning / data mining field thanks to curiosity generated by Toby Segaran's classic Programming Collective Intelligence: Building Smart Web 2.0 Applications . The book by Drew Conway and John White continues in the same excellent tradition. It presents case studies which are interesting enough that you can appreciate them without too much domain knowledge and without getting too deep into technical nitty-gritty. At the same time, the case studies are meaty enough that you can adapt them to real life problems and hack together a quick working prototype in your practice.

  By many estimates (and my own experience), 80% of time in machine learning is spent in data cleaning and exploratory data analysis. This book has very good coverage of both areas. Authors use Hadley Wickham's excellent packages viz. ggplot2, plyr and reshape2. If you are doing serious exploratory data analysis in R, these packages are a must and the book does a great job in showing them in action.

  The reason I suffixed the review with 'if you know a little R' is that data cleansing requires one to be fairly comfortable with somewhat arcane R syntax. If you don't know any R at all, it would be helpful to get a more gentle introduction - such as R Cookbook (O'Reilly Cookbooks) - before you tackle this book.

  In summary, this is an excellent 2nd book on R to have as you try to deploy machine learning in real life.
  BTW, if you are looking for 3rd R book, my vote is Data Mining with R: Learning with Case Studies (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
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  Martin Castillo Cabrera
  5.0 out of 5 stars Temas acordes
  Reviewed in Mexico on April 13, 2019
  Verified Purchase
  Excelente libro
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  Sai Sampath
  5.0 out of 5 stars The book is real good but I wouldn't prefer it if you are new ...
  Reviewed in India on December 13, 2017
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  The book is real good but I wouldn't prefer it if you are new to machine learning. You would find it to be real good if have basic knowledge of the subject
  Read more
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  Carlo
  5.0 out of 5 stars Esauriente
  Reviewed in Italy on May 3, 2017
  Verified Purchase
  Un buon libro per chi si vuole avvicinare al Machine Learning, il taglio della spiegazione è americano quindi con esempi significativi.
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  Bobobob
  1.0 out of 5 stars Titel ist vorsätzliche Täuschung
  Reviewed in Germany on October 21, 2014
  Verified Purchase
  Kann ich nur dringend von abraten. Klare vorsätzliche Täuschung der Titel. Es wir einem nicht erklärt was Machine Learning ist, es wird nur R benutzt die Algorithmen zu berechnen oder zu visualisieren und es wird weder R noch maschinelles Lernen erklärt.
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  Kosako2007
  5.0 out of 5 stars Genial introducción al mundo del Machine Learning
  Reviewed in Spain on February 23, 2013
  Verified Purchase
  El libro es una muy buena introducción al Machine Learning y los conceptos en los que se basa. Lo recomiendo para todo aquel desarrollador que quiera introducirse en ese mundo.
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 Machine Learning for Hackers: Case Studies and Algorithms to Get You Started First Edition
 by Drew Conway (Author), John Myles White
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 If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning―a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.
 Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.
 * Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text
 * Use linear regression to predict the number of page views for the top 1,000 websites
 * Learn optimization techniques by attempting to break a simple letter cipher
 * Compare and contrast U.S. Senators statistically, based on their voting records
 * Build a “whom to follow” recommendation system from Twitter data
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 1449303714
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 978-1449303716
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 5. Publication date
 March 20, 2012
 6. Language
 English
 7. Dimensions
 7 x 0.75 x 9.19 inches
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 320 pages
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 About the Author
 Drew Conway is a PhD candidate in Politics at NYU. He studies international relations, conflict, and terrorism using the tools of mathematics, statistics, and computer science in an attempt to gain a deeper understanding of these phenomena. His academic curiosity is informed by his years as an analyst in the U.S. intelligence and defense communities.
 John Myles White is a PhD candidate in Psychology at Princeton. He studies pattern recognition, decision-making, and economic behavior using behavioral methods and fMRI. He is particularly interested in anomalies of value assessment.
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 * Publisher ‏ : ‎ O'Reilly; First Edition (March 20, 2012)
 * Language ‏ : ‎ English
 * Paperback ‏ : ‎ 320 pages
 * ISBN-10 ‏ : ‎ 1449303714
 * ISBN-13 ‏ : ‎ 978-1449303716
 * Item Weight ‏ : ‎ 1.14 pounds
 * Dimensions ‏ : ‎ 7 x 0.75 x 9.19 inches
 * Best Sellers Rank: #1,114,970 in Books ( See Top 100 in Books )
 + #208 in Machine Theory (Books)
 + #375 in Natural Language Processing (Books)
 + #740 in Computer Hacking
 * Customer Reviews:
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  3.7 out of 5 stars
  3.7 out of 5
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  W. Hogan
  5.0 out of 5 stars An interesting and easy read
  Reviewed in the United States on December 12, 2013
  Verified Purchase
  I enjoyed reading this book.
  Pro's:
  The text is parsimonious.
  The examples are interesting.
  The coding is clever.
  The book is less expensive and easier to understand than most Springer texts.
  Con's:
  A substantial part of the code is peripheral tasks; this can be skipped.
  Some of the code is out of date.
  These Con's are trivial. The book is great. I would buy any other books written by these authors.
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  2 people found this helpful
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  Graham
  2.0 out of 5 stars Entry Level R Programming Book
  Reviewed in the United States on November 13, 2016
  Verified Purchase
  This text offers a detailed description in each of 10 case studies about how to build a machine learning solution to the particular problem mentioned. The authors do this in R, and are extensively descriptive about the mechanics of writing R code. If you've never written a computer program, but want to understand how to implement a prewritten machine learning tool in R, this book could be of assistance. However, I'm not sure the authors actually understand the mathematical theory of machine learning; in this book they constantly substitute descriptions of how to select appropriate algorithm parameters with a trial-and-error approach, they do not explain how the algorithms work, and I'm not sure they ever mention the mechanics of "learning" with respect to mathematics. The book has brief passages about machine learning hidden amongst vast chapters about how to read computer directories and load data, etc. Again this book could be useful to the reader who understands ML, but not computer programming.
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  4 people found this helpful
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  Chris
  4.0 out of 5 stars Learned something new
  Reviewed in the United States on October 2, 2012
  Verified Purchase
  I learned a little about the R language and how a lot of scientist of physicist use it the write functions to dynamically create graphs to analyze the data they compiled. It also encompasses a lot of the same math you learn in statistics classes which makes sense. I even ended up downloading an R language reference app from the Apple app store to have an object reference of the language.
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  Voracious Reader
  3.0 out of 5 stars Machine Learning for Non-Hackers
  Reviewed in the United States on March 21, 2012
  Verified Purchase
  By page count, this is primarily a book on R, with some additional time spent on machine learning.
  There is way too much time spent on R, dedicated to such things as parsing email messages, and spidering webpages, etc. These are things that no-one with other tools available would do in R. And it's not that it's easier to do it in R, it's actually harder than using an appropriate library, like JavaMail. And yet, while much time is spent in details, like regexes to extract dates (ick!), more interesting R functions are given short shrift.
  There's some good material in here, but it's buried under the weight of doing everything in R. If you are a non-programmer, and want to use only one hammer for everything, then R is not a bad choice. But it's not a good choice for developers that are already comfortable with a wider variety of tools.
  I'd recommend Programming Collective Intelligence by Segaran, if you would describe yourself as a "Hacker".
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  145 people found this helpful
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  Ravi Aranke
  5.0 out of 5 stars Excellent and immediately practical, if you already know some R
  Reviewed in the United States on February 20, 2012
  I started my journey in the machine learning / data mining field thanks to curiosity generated by Toby Segaran's classic Programming Collective Intelligence: Building Smart Web 2.0 Applications . The book by Drew Conway and John White continues in the same excellent tradition. It presents case studies which are interesting enough that you can appreciate them without too much domain knowledge and without getting too deep into technical nitty-gritty. At the same time, the case studies are meaty enough that you can adapt them to real life problems and hack together a quick working prototype in your practice.
  By many estimates (and my own experience), 80% of time in machine learning is spent in data cleaning and exploratory data analysis. This book has very good coverage of both areas. Authors use Hadley Wickham's excellent packages viz. ggplot2, plyr and reshape2. If you are doing serious exploratory data analysis in R, these packages are a must and the book does a great job in showing them in action.
  The reason I suffixed the review with 'if you know a little R' is that data cleansing requires one to be fairly comfortable with somewhat arcane R syntax. If you don't know any R at all, it would be helpful to get a more gentle introduction - such as R Cookbook (O'Reilly Cookbooks) - before you tackle this book.
  In summary, this is an excellent 2nd book on R to have as you try to deploy machine learning in real life.
  BTW, if you are looking for 3rd R book, my vote is Data Mining with R: Learning with Case Studies (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
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  Martin Castillo Cabrera
  5.0 out of 5 stars Temas acordes
  Reviewed in Mexico on April 13, 2019
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  Excelente libro
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  Sai Sampath
  5.0 out of 5 stars The book is real good but I wouldn't prefer it if you are new ...
  Reviewed in India on December 13, 2017
  Verified Purchase
  The book is real good but I wouldn't prefer it if you are new to machine learning. You would find it to be real good if have basic knowledge of the subject
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  Carlo
  5.0 out of 5 stars Esauriente
  Reviewed in Italy on May 3, 2017
  Verified Purchase
  Un buon libro per chi si vuole avvicinare al Machine Learning, il taglio della spiegazione è americano quindi con esempi significativi.
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  Bobobob
  1.0 out of 5 stars Titel ist vorsätzliche Täuschung
  Reviewed in Germany on October 21, 2014
  Verified Purchase
  Kann ich nur dringend von abraten. Klare vorsätzliche Täuschung der Titel. Es wir einem nicht erklärt was Machine Learning ist, es wird nur R benutzt die Algorithmen zu berechnen oder zu visualisieren und es wird weder R noch maschinelles Lernen erklärt.
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  Kosako2007
  5.0 out of 5 stars Genial introducción al mundo del Machine Learning
  Reviewed in Spain on February 23, 2013
  Verified Purchase
  El libro es una muy buena introducción al Machine Learning y los conceptos en los que se basa. Lo recomiendo para todo aquel desarrollador que quiera introducirse en ese mundo.
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Machine Learning for Hackers: Case Studies and Algorithms to Get You Started: Conway, Drew, White, John Myles: 9781449303716: Amazon.com: Books
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Pattern Recognition and Machine Learning (Information Science and Statistics): Bishop, Christopher M.: 9780387310732: Amazon.com: Books
2006-08-17
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 This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

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 1. ISBN-10
 0387310738
 2. ISBN-13
 978-0387310732
 3. Publisher
 Springer
 4. Publication date
 August 17, 2006
 5. Language
 English
 6. Dimensions
 7.25 x 1.75 x 9.25 inches
 7. Print length
 738 pages
 8. See all details
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 Editorial Reviews

 Review

 From the reviews:

 "This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group." John Maindonald for the Journal of Statistical Software

 "In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ‘pattern recognition’ or ‘machine learning’. … This book will serve as an excellent reference. … With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop’s book is a useful introduction … and a valuable reference for the principle techniques used in these fields." (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007)

 "This book appears in the Information Science and Statistics Series commissioned by the publishers. … The book appears to have been designed for course teaching, but obviously contains material that readers interested in self-study can use. It is certainly structured for easy use. … For course teachers there is ample backing which includes some 400 exercises. … it does contain important material which can be easily followed without the reader being confined to a pre-determined course of study." (W. R. Howard, Kybernetes, Vol. 36 (2), 2007)

 "Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. Aimed at advanced undergraduates and first-year graduate students, as well as researchers and practitioners, the book assumes knowledge of multivariate calculus and linear algebra … . SummingUp: Highly recommended. Upper-division undergraduates through professionals." (C. Tappert, CHOICE, Vol. 44 (9), May, 2007)

 "The book is structured into 14 main parts and 5 appendices. … The book is aimed at PhD students, researchers and practitioners. It is well-suited for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bio-informatics. Extensive support is provided for course instructors, including more than 400 exercises, lecture slides and a great deal of additional material available at the book’s web site … ." (Ingmar Randvee, Zentralblatt MATH, Vol. 1107 (9), 2007)

 "This new textbook by C. M. Bishop is a brilliant extension of his former book ‘Neural Networks for Pattern Recognition’. It is written for graduate students or scientists doing interdisciplinary work in related fields. … In summary, this textbook is an excellent introduction to classical pattern recognition and machine learning (in the senseof parameter estimation). A large number of very instructive illustrations adds to this value." (H. G. Feichtinger, Monatshefte für Mathematik, Vol. 151 (3), 2007)

 "Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. … Pattern Recognition and Machine Learning provides excellent intuitive descriptions and appropriate-level technical details on modern pattern recognition and machine learning. It can be used to teach a course or for self-study, as well as for a reference. … I strongly recommend it for the intended audience and note that Neal (2007) also has given this text a strong review to complement its strong sales record." (Thomas Burr, Journal of the American Statistical Association, Vol. 103 (482), June, 2008)

 "This accessible monograph seeks to provide a comprehensive introduction to the fields of pattern recognition and machine learning. It presents a unified treatment of well-known statistical pattern recognition techniques. … The book can be used by advanced undergraduates and graduate students … . The illustrative examples and exercises proposed at the end of each chapter are welcome … . The book, which provides several new views, developments and results, is appropriate for both researchers and students who work in machine learning … ." (L. State, ACM Computing Reviews, October, 2008)

 "Chris Bishop’s … technical exposition that is at once lucid and mathematically rigorous. … In more than 700 pages of clear, copiously illustrated text, he develops a common statistical framework that encompasses … machine learning. … it is a textbook, with a wide range of exercises, instructions to tutors on where to go for full solutions, and the color illustrations that have become obligatory in undergraduate texts. … its clarity and comprehensiveness will make it a favorite desktop companion for practicing data analysts." (H. Van Dyke Parunak, ACM Computing Reviews, Vol. 49 (3), March, 2008)

 From the Back Cover

 The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.

 This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

 The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

 Christopher M. Bishop is Deputy Director of Microsoft Research Cambridge, and holds a Chair inComputer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society of Edinburgh. His previous textbook "Neural Networks for Pattern Recognition" has been widely adopted.

 Coming soon:

 *For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)

 *For instructors, worked solutions to remaining exercises from the Springer web site

 *Lecture slides to accompany each chapter

 *Data sets available for download



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 Product details

 * Publisher ‏ : ‎ Springer (August 17, 2006)
 * Language ‏ : ‎ English
 * Hardcover ‏ : ‎ 738 pages
 * ISBN-10 ‏ : ‎ 0387310738
 * ISBN-13 ‏ : ‎ 978-0387310732
 * Item Weight ‏ : ‎ 2.31 pounds
 * Dimensions ‏ : ‎ 7.25 x 1.75 x 9.25 inches
 * Best Sellers Rank: #104,080 in Books ( See Top 100 in Books )
 + #15 in Computer Vision & Pattern Recognition
 + #98 in Probability & Statistics (Books)
 + #204 in Artificial Intelligence & Semantics
 * Customer Reviews:
 4.6 4.6 out of 5 stars 740 ratings
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 Christopher M. Bishop

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 Christopher Michael Bishop (born 7 April 1959) FREng, FRSE, is the Laboratory Director at Microsoft Research Cambridge and professor of Computer Science at the University of Edinburgh and a Fellow of Darwin College, Cambridge. Chris obtained a Bachelor of Arts degree in Physics from St Catherine's College, Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory.

 Bio from Wikipedia, the free encyclopedia. Photo by MSRCambridge (Own work) [CC BY-SA 4.0 (http://creativecommons.org/licenses/by-sa/4.0)], via Wikimedia Commons.

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  Quality issues
  The book came in good quality, pages very glossy etc. Except the binding was way off???? Its not a major issue right now but the front page will rip if i try to pull it off correctly. Wouldnt be an issue, but literally all the pages haven't been glued in properly aligned to the hardcover. Disappointing......
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  Kinga
  5.0 out of 5 stars Excellent text
  Reviewed in the United States on February 22, 2008
  Verified Purchase
  First of all, as some other reviewers have pointed out, the subtitle of the book should include the word 'Bayesian' in some form or the other. The reason this is important is because the Bayesian approach, although an important one, is not adapted across the board in machine learning, and consequently, an astonishing number of methods presented in the book (Bayesian versions of just about anything) are not mainstream. The recent Duda book gives a better idea of the mainstream in this sense, but because the field has evolved in such rapidity, it excludes massive recent developments in kernel methods and graphical models, which Bishop includes.

  Pedagogically, however, this book is almost uniformly excellent. I didn't like the presentation on some of the material (the first few sections on linear classification are relatively poor), but in general, Bishop does an amazing job. If you want to learn the mathematical base of most machine learning methods in a practical and reasonably rigorous way, this book is for you. Pay attention in particular to the exercises, which are the best I've seen so far in such a text; involved, but not frustrating, and always aiming to further elucidate the concepts. If you want to really learn the material presented, you should, at the very least, solve all the exercises that appear in the sections of the text (about half of the total). I've gone through almost the entire text, and done just that, so I can say that it's not as daunting as it looks. To judge your level regarding this, solve the exercises for the first two chapters (the second, a sort of crash course on probability, is quite formidable). If you can do these, you should be fine. The author has solutions for a lot of them on his website, so you can go there and check if you get stuck on some.

  As far as the Bayesian methods are concerned, they are usually a lot more mathematically involved than their counterparts, so solving the equations representing them can only give you more practice. Seeing the same material in a different light can never hurt you, and I learned some important statistical/mathematical concepts from the book that I'd never heard of, such as the Laplace and Evidence Approximations. Of course, if you're not interested, you can simply skip the method altogether.

  From the preceding, it should be clear that the book is written for a certain kind of reader in mind. It is not for people who want a quick introduction to some method without the gory details behind its mathematical machinery. There is no pseudocode. The book assumes that once you get the math, the algorithm to implement the method should either become completely clear, or in the case of some more complicated methods (SVMs for example), you know where to head for details on an implementation. Therefore, the people who will benefit most from the book are those who will either be doing research in this area, or will be implementing the methods in detail on lower level languages (such as C). I know that sounds offputting, but the good thing is that the level of the math required to understand the methods is quite low; basic probability, linear algebra and multivariable calculus. (Read the appendices in detail as well.) No knowledge is needed, for example, of measure-theoretic probability or function spaces (for kernel methods) etc. Therefore the book is accessible to most with a decent engineering background, who are willing to work through it. If you're one of the people who the book is aimed at, you should seriously consider getting it.

  Edited to Add:
  I've changed my rating from 4 stars to 5. Even now, 4-5 years later, there is simply no good substitute for this book.
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  Mlungisi
  5.0 out of 5 stars Towards Machine Learning
  Reviewed in the United States on March 22, 2024
  Verified Purchase
  Quite helpful and been able to solve machine learning problems
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  Scott Davies
  5.0 out of 5 stars If only all textbooks were this well-written
  Reviewed in the United States on January 29, 2007
  Verified Purchase
  I was a big fan of Bishop's earlier "Neural Networks for Pattern Recognition" despite my not being particularly interested in neural networks (as opposed to other aspects of machine learning), and so I was pretty excited when I heard about this book. Reading it has not left me disappointed. Like his earlier book, this text is quite mathematically oriented, and not well-suited for people who aren't comfortable with calculus. However, also like in "NNPR", the writing style here is very clear, and everything past basic calculus and linear algebra is well-explained before it's needed. The appendices alone are a goldmine. (Appendix B is a great "cheat sheet" for commonly used probability distributions; Appendix C has lots of useful matrix properties you may have forgotten or never known; Appendix D quickly explains what you need to know about the calculus of variations; and Appendix E does the same for Lagrange multipliers.) The author also does an excellent job throughout the text of marrying math and intuition without giving either short shrift.

  However, note that the material covered is inherently pretty complex, so the book can still be intimidating in parts despite the excellent writing. It's more appropriate for, say, Ph.D. students and professional researchers in statistics or machine learning than people who just want to crank out code for a simple classifier. There is very little pseudocode (although copious MATLAB code will supposedly be made available in a companion book due out in 2008), and the book's overall approach to machine learning is basically hard-core Bayesian statistics. If you are not willing to scratch your head for a while over lots and lots of equations, this may not be the book for you.

  On the flip side, people who are already experts in machine learning may be mildly disappointed with the lack of coverage some of their pet topics get. For example, while the chapter on graphical models is excellent as far as it goes, it only mentions the problem of learning graphical model structures (one of my areas of interest) in passing. Reinforcement learning (another personal area of interest) is discussed briefly in the introduction and then written off as beyond the scope of the book.

  However, the book is already a fabulous resource as it stands; complaining there's not even more of it would be gauche. The cover may look like goat barf, and there are some innocuous missing words here and there (hey, it's a first edition), but if you're serious about machine learning and not afraid of a little math, you should definitely own this book. I can only imagine how much cooler my own thesis research might have been if this book had been around a few years earlier.
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  J. Elliott
  4.0 out of 5 stars Not for beginners or want to be's
  Reviewed in the United States on April 4, 2013
  Verified Purchase
  The book is not written for a beginner or even an intermediate researcher. This is a very serious book on Pattern Recognition and Machine Learning. If you are looking for computer code you can lift and use in your home grown system, this is not in the book for you. If you have not had some serious math courses beyond the basics. You will quickly be lost. I have had 15 graduate math and statistics classes and work in IT currently. I purchased this book to help me with a research project I am doing on my own with pattern recognition where machine learning is important to the process. Many of my own equations as I did my research appeared in this book. I am using a relational database to store information and remember patterns. If you don't have experience with numerical methods and turning equations in to computer code and can do it in your head. You will have great deal of difficulty with this book. That said as a warning, now to the book. It is a great reference and spends a great deal of time several statistical theories and their practical uses in signal and pattern recognition. It also has various ways for doing curve fitting and dealing with weighted neural networks. While this is written as a text book, it is more of reference book. I do not see how even a years course could work on all the information in this book. One of my chief pet peeves is that text book publishers have websites and answers only professors can get to. How about those of us who are finished with school and have no intention of ever going back? So to the publisher Springer I give one star and zero if I could! This is a reference book and most likely will never be a text book. This is true with this book. Which is why I rate it four instead of five stars because of the publisher.
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  Tavo Cruzs
  5.0 out of 5 stars Excelente libro
  Reviewed in Mexico on February 27, 2022
  Verified Purchase
  - Contenido del libro: 5 estrella
  El contenido del libro esta muy bien desarrollado, trata los temas que todo Científico de Datos debe conocer.

  - Estado físico del libro: 5 estrellas
  El libro llegó en excelentes condiciones. Sobra decirlo, pero es nuevo. Me gustó que las ilustraciones están a colores y las hojas están como texturizadas, se siente la calidad. Por el precio de $999 tealmente es una ganga.

  Estoy satisfecho por esta compra, realmente superó mis espectativas este libro, tanto por su contenido, como por su calidad en el papel.
  Read more
  Tavo Cruzs
  5.0 out of 5 stars Excelente libro
  Reviewed in Mexico on February 27, 2022
  - Contenido del libro: 5 estrella
  El contenido del libro esta muy bien desarrollado, trata los temas que todo Científico de Datos debe conocer.

  - Estado físico del libro: 5 estrellas
  El libro llegó en excelentes condiciones. Sobra decirlo, pero es nuevo. Me gustó que las ilustraciones están a colores y las hojas están como texturizadas, se siente la calidad. Por el precio de $999 tealmente es una ganga.

  Estoy satisfecho por esta compra, realmente superó mis espectativas este libro, tanto por su contenido, como por su calidad en el papel.
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  Ana Isabel Bezerra Cavalcanti
  5.0 out of 5 stars Uma combinação de desenvolvimentos matemáticos e estatísticos aplicados às duas áreas
  Reviewed in Brazil on January 21, 2022
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  Utilizo esse produto em pesquisas acadêmicas.
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  Ashis Sardar
  5.0 out of 5 stars Original hard bind high quality color print hardcopy
  Reviewed in India on June 13, 2023
  Verified Purchase
  You can rely on this purchase though by searching with book name on search bar of Amazon app you won't get that deal visible, got to know from google. Bought it in 2019/-
  Price justified with product.. Other sellers selling with way high price still in paperpack with black and white print.
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  Ashis Sardar
  5.0 out of 5 stars Original hard bind high quality color print hardcopy
  Reviewed in India on June 13, 2023
  You can rely on this purchase though by searching with book name on search bar of Amazon app you won't get that deal visible, got to know from google. Bought it in 2019/-
  Price justified with product.. Other sellers selling with way high price still in paperpack with black and white print.
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  T. Lux
  5.0 out of 5 stars Excellent
  Reviewed in Spain on April 7, 2020
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  I liked this book a lot as an introduction to the topic.
  It helped me significantly to understand better the different concepts.
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  Giorgio
  5.0 out of 5 stars Le basi del machine learning!
  Reviewed in Italy on July 6, 2019
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  Comprato per l’università questo libro offre una visione completa sul machine learning ed è consigliato a chiunque sia interessato all argomento. Il libro è molto espansivo. Io L ho acquistato con copertina rigida ed è davvero bello. Inoltre le stampe sono a colori e permettono di comprendere meglio di altri libri i grafici e le immagini proposte.
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 Pattern Recognition and Machine Learning (Information Science and Statistics)
 by Christopher M. Bishop (Author)
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 This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
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 1. ISBN-10
 0387310738
 2. ISBN-13
 978-0387310732
 3. Publisher
 Springer
 4. Publication date
 August 17, 2006
 5. Language
 English
 6. Dimensions
 7.25 x 1.75 x 9.25 inches
 7. Print length
 738 pages
 8. See all details
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 Editorial Reviews
 Review
 From the reviews:
 "This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group." John Maindonald for the Journal of Statistical Software
 "In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ‘pattern recognition’ or ‘machine learning’. … This book will serve as an excellent reference. … With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop’s book is a useful introduction … and a valuable reference for the principle techniques used in these fields." (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007)
 "This book appears in the Information Science and Statistics Series commissioned by the publishers. … The book appears to have been designed for course teaching, but obviously contains material that readers interested in self-study can use. It is certainly structured for easy use. … For course teachers there is ample backing which includes some 400 exercises. … it does contain important material which can be easily followed without the reader being confined to a pre-determined course of study." (W. R. Howard, Kybernetes, Vol. 36 (2), 2007)
 "Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. Aimed at advanced undergraduates and first-year graduate students, as well as researchers and practitioners, the book assumes knowledge of multivariate calculus and linear algebra … . SummingUp: Highly recommended. Upper-division undergraduates through professionals." (C. Tappert, CHOICE, Vol. 44 (9), May, 2007)
 "The book is structured into 14 main parts and 5 appendices. … The book is aimed at PhD students, researchers and practitioners. It is well-suited for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bio-informatics. Extensive support is provided for course instructors, including more than 400 exercises, lecture slides and a great deal of additional material available at the book’s web site … ." (Ingmar Randvee, Zentralblatt MATH, Vol. 1107 (9), 2007)
 "This new textbook by C. M. Bishop is a brilliant extension of his former book ‘Neural Networks for Pattern Recognition’. It is written for graduate students or scientists doing interdisciplinary work in related fields. … In summary, this textbook is an excellent introduction to classical pattern recognition and machine learning (in the senseof parameter estimation). A large number of very instructive illustrations adds to this value." (H. G. Feichtinger, Monatshefte für Mathematik, Vol. 151 (3), 2007)
 "Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. … Pattern Recognition and Machine Learning provides excellent intuitive descriptions and appropriate-level technical details on modern pattern recognition and machine learning. It can be used to teach a course or for self-study, as well as for a reference. … I strongly recommend it for the intended audience and note that Neal (2007) also has given this text a strong review to complement its strong sales record." (Thomas Burr, Journal of the American Statistical Association, Vol. 103 (482), June, 2008)
 "This accessible monograph seeks to provide a comprehensive introduction to the fields of pattern recognition and machine learning. It presents a unified treatment of well-known statistical pattern recognition techniques. … The book can be used by advanced undergraduates and graduate students … . The illustrative examples and exercises proposed at the end of each chapter are welcome … . The book, which provides several new views, developments and results, is appropriate for both researchers and students who work in machine learning … ." (L. State, ACM Computing Reviews, October, 2008)
 "Chris Bishop’s … technical exposition that is at once lucid and mathematically rigorous. … In more than 700 pages of clear, copiously illustrated text, he develops a common statistical framework that encompasses … machine learning. … it is a textbook, with a wide range of exercises, instructions to tutors on where to go for full solutions, and the color illustrations that have become obligatory in undergraduate texts. … its clarity and comprehensiveness will make it a favorite desktop companion for practicing data analysts." (H. Van Dyke Parunak, ACM Computing Reviews, Vol. 49 (3), March, 2008)
 From the Back Cover
 The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.
 This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
 The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.
 Christopher M. Bishop is Deputy Director of Microsoft Research Cambridge, and holds a Chair inComputer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society of Edinburgh. His previous textbook "Neural Networks for Pattern Recognition" has been widely adopted.
 Coming soon:
 *For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)
 *For instructors, worked solutions to remaining exercises from the Springer web site
 *Lecture slides to accompany each chapter
 *Data sets available for download
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 Product details
 * Publisher ‏ : ‎ Springer (August 17, 2006)
 * Language ‏ : ‎ English
 * Hardcover ‏ : ‎ 738 pages
 * ISBN-10 ‏ : ‎ 0387310738
 * ISBN-13 ‏ : ‎ 978-0387310732
 * Item Weight ‏ : ‎ 2.31 pounds
 * Dimensions ‏ : ‎ 7.25 x 1.75 x 9.25 inches
 * Best Sellers Rank: #104,080 in Books ( See Top 100 in Books )
 + #15 in Computer Vision & Pattern Recognition
 + #98 in Probability & Statistics (Books)
 + #204 in Artificial Intelligence & Semantics
 * Customer Reviews:
 4.6 4.6 out of 5 stars 740 ratings
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 Christopher M. Bishop
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 Christopher Michael Bishop (born 7 April 1959) FREng, FRSE, is the Laboratory Director at Microsoft Research Cambridge and professor of Computer Science at the University of Edinburgh and a Fellow of Darwin College, Cambridge. Chris obtained a Bachelor of Arts degree in Physics from St Catherine's College, Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory.
 Bio from Wikipedia, the free encyclopedia. Photo by MSRCambridge (Own work) [CC BY-SA 4.0 (http://creativecommons.org/licenses/by-sa/4.0)], via Wikimedia Commons.
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  The book came in good quality, pages very glossy etc. Except the binding was way off???? Its not a major issue right now but the front page will rip if i try to pull it off correctly. Wouldnt be an issue, but literally all the pages haven't been glued in properly aligned to the hardcover. Disappointing......
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  Kinga
  5.0 out of 5 stars Excellent text
  Reviewed in the United States on February 22, 2008
  Verified Purchase
  First of all, as some other reviewers have pointed out, the subtitle of the book should include the word 'Bayesian' in some form or the other. The reason this is important is because the Bayesian approach, although an important one, is not adapted across the board in machine learning, and consequently, an astonishing number of methods presented in the book (Bayesian versions of just about anything) are not mainstream. The recent Duda book gives a better idea of the mainstream in this sense, but because the field has evolved in such rapidity, it excludes massive recent developments in kernel methods and graphical models, which Bishop includes.
  Pedagogically, however, this book is almost uniformly excellent. I didn't like the presentation on some of the material (the first few sections on linear classification are relatively poor), but in general, Bishop does an amazing job. If you want to learn the mathematical base of most machine learning methods in a practical and reasonably rigorous way, this book is for you. Pay attention in particular to the exercises, which are the best I've seen so far in such a text; involved, but not frustrating, and always aiming to further elucidate the concepts. If you want to really learn the material presented, you should, at the very least, solve all the exercises that appear in the sections of the text (about half of the total). I've gone through almost the entire text, and done just that, so I can say that it's not as daunting as it looks. To judge your level regarding this, solve the exercises for the first two chapters (the second, a sort of crash course on probability, is quite formidable). If you can do these, you should be fine. The author has solutions for a lot of them on his website, so you can go there and check if you get stuck on some.
  As far as the Bayesian methods are concerned, they are usually a lot more mathematically involved than their counterparts, so solving the equations representing them can only give you more practice. Seeing the same material in a different light can never hurt you, and I learned some important statistical/mathematical concepts from the book that I'd never heard of, such as the Laplace and Evidence Approximations. Of course, if you're not interested, you can simply skip the method altogether.
  From the preceding, it should be clear that the book is written for a certain kind of reader in mind. It is not for people who want a quick introduction to some method without the gory details behind its mathematical machinery. There is no pseudocode. The book assumes that once you get the math, the algorithm to implement the method should either become completely clear, or in the case of some more complicated methods (SVMs for example), you know where to head for details on an implementation. Therefore, the people who will benefit most from the book are those who will either be doing research in this area, or will be implementing the methods in detail on lower level languages (such as C). I know that sounds offputting, but the good thing is that the level of the math required to understand the methods is quite low; basic probability, linear algebra and multivariable calculus. (Read the appendices in detail as well.) No knowledge is needed, for example, of measure-theoretic probability or function spaces (for kernel methods) etc. Therefore the book is accessible to most with a decent engineering background, who are willing to work through it. If you're one of the people who the book is aimed at, you should seriously consider getting it.
  Edited to Add:
  I've changed my rating from 4 stars to 5. Even now, 4-5 years later, there is simply no good substitute for this book.
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  Mlungisi
  5.0 out of 5 stars Towards Machine Learning
  Reviewed in the United States on March 22, 2024
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  Quite helpful and been able to solve machine learning problems
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  Scott Davies
  5.0 out of 5 stars If only all textbooks were this well-written
  Reviewed in the United States on January 29, 2007
  Verified Purchase
  I was a big fan of Bishop's earlier "Neural Networks for Pattern Recognition" despite my not being particularly interested in neural networks (as opposed to other aspects of machine learning), and so I was pretty excited when I heard about this book. Reading it has not left me disappointed. Like his earlier book, this text is quite mathematically oriented, and not well-suited for people who aren't comfortable with calculus. However, also like in "NNPR", the writing style here is very clear, and everything past basic calculus and linear algebra is well-explained before it's needed. The appendices alone are a goldmine. (Appendix B is a great "cheat sheet" for commonly used probability distributions; Appendix C has lots of useful matrix properties you may have forgotten or never known; Appendix D quickly explains what you need to know about the calculus of variations; and Appendix E does the same for Lagrange multipliers.) The author also does an excellent job throughout the text of marrying math and intuition without giving either short shrift.
  However, note that the material covered is inherently pretty complex, so the book can still be intimidating in parts despite the excellent writing. It's more appropriate for, say, Ph.D. students and professional researchers in statistics or machine learning than people who just want to crank out code for a simple classifier. There is very little pseudocode (although copious MATLAB code will supposedly be made available in a companion book due out in 2008), and the book's overall approach to machine learning is basically hard-core Bayesian statistics. If you are not willing to scratch your head for a while over lots and lots of equations, this may not be the book for you.
  On the flip side, people who are already experts in machine learning may be mildly disappointed with the lack of coverage some of their pet topics get. For example, while the chapter on graphical models is excellent as far as it goes, it only mentions the problem of learning graphical model structures (one of my areas of interest) in passing. Reinforcement learning (another personal area of interest) is discussed briefly in the introduction and then written off as beyond the scope of the book.
  However, the book is already a fabulous resource as it stands; complaining there's not even more of it would be gauche. The cover may look like goat barf, and there are some innocuous missing words here and there (hey, it's a first edition), but if you're serious about machine learning and not afraid of a little math, you should definitely own this book. I can only imagine how much cooler my own thesis research might have been if this book had been around a few years earlier.
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  J. Elliott
  4.0 out of 5 stars Not for beginners or want to be's
  Reviewed in the United States on April 4, 2013
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  The book is not written for a beginner or even an intermediate researcher. This is a very serious book on Pattern Recognition and Machine Learning. If you are looking for computer code you can lift and use in your home grown system, this is not in the book for you. If you have not had some serious math courses beyond the basics. You will quickly be lost. I have had 15 graduate math and statistics classes and work in IT currently. I purchased this book to help me with a research project I am doing on my own with pattern recognition where machine learning is important to the process. Many of my own equations as I did my research appeared in this book. I am using a relational database to store information and remember patterns. If you don't have experience with numerical methods and turning equations in to computer code and can do it in your head. You will have great deal of difficulty with this book. That said as a warning, now to the book. It is a great reference and spends a great deal of time several statistical theories and their practical uses in signal and pattern recognition. It also has various ways for doing curve fitting and dealing with weighted neural networks. While this is written as a text book, it is more of reference book. I do not see how even a years course could work on all the information in this book. One of my chief pet peeves is that text book publishers have websites and answers only professors can get to. How about those of us who are finished with school and have no intention of ever going back? So to the publisher Springer I give one star and zero if I could! This is a reference book and most likely will never be a text book. This is true with this book. Which is why I rate it four instead of five stars because of the publisher.
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  - Contenido del libro: 5 estrella
  El contenido del libro esta muy bien desarrollado, trata los temas que todo Científico de Datos debe conocer.
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  El libro llegó en excelentes condiciones. Sobra decirlo, pero es nuevo. Me gustó que las ilustraciones están a colores y las hojas están como texturizadas, se siente la calidad. Por el precio de $999 tealmente es una ganga.
  Estoy satisfecho por esta compra, realmente superó mis espectativas este libro, tanto por su contenido, como por su calidad en el papel.
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  Tavo Cruzs
  5.0 out of 5 stars Excelente libro
  Reviewed in Mexico on February 27, 2022
  - Contenido del libro: 5 estrella
  El contenido del libro esta muy bien desarrollado, trata los temas que todo Científico de Datos debe conocer.
  - Estado físico del libro: 5 estrellas
  El libro llegó en excelentes condiciones. Sobra decirlo, pero es nuevo. Me gustó que las ilustraciones están a colores y las hojas están como texturizadas, se siente la calidad. Por el precio de $999 tealmente es una ganga.
  Estoy satisfecho por esta compra, realmente superó mis espectativas este libro, tanto por su contenido, como por su calidad en el papel.
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  5.0 out of 5 stars Uma combinação de desenvolvimentos matemáticos e estatísticos aplicados às duas áreas
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  Utilizo esse produto em pesquisas acadêmicas.
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  Ashis Sardar
  5.0 out of 5 stars Original hard bind high quality color print hardcopy
  Reviewed in India on June 13, 2023
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  You can rely on this purchase though by searching with book name on search bar of Amazon app you won't get that deal visible, got to know from google. Bought it in 2019/-
  Price justified with product.. Other sellers selling with way high price still in paperpack with black and white print.
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  Ashis Sardar
  5.0 out of 5 stars Original hard bind high quality color print hardcopy
  Reviewed in India on June 13, 2023
  You can rely on this purchase though by searching with book name on search bar of Amazon app you won't get that deal visible, got to know from google. Bought it in 2019/-
  Price justified with product.. Other sellers selling with way high price still in paperpack with black and white print.
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  T. Lux
  5.0 out of 5 stars Excellent
  Reviewed in Spain on April 7, 2020
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  I liked this book a lot as an introduction to the topic.
  It helped me significantly to understand better the different concepts.
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  Giorgio
  5.0 out of 5 stars Le basi del machine learning!
  Reviewed in Italy on July 6, 2019
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  Comprato per l’università questo libro offre una visione completa sul machine learning ed è consigliato a chiunque sia interessato all argomento. Il libro è molto espansivo. Io L ho acquistato con copertina rigida ed è davvero bello. Inoltre le stampe sono a colori e permettono di comprendere meglio di altri libri i grafici e le immagini proposte.
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Pattern Recognition and Machine Learning (Information Science and Statistics): Bishop, Christopher M.: 9780387310732: Amazon.com: Books
https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738?tag=hackr-20&geniuslink=true
https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020?tag=hackr-20&geniuslink=true
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series): Murphy, Kevin P.: 9780262018029: Amazon.com: Books
2012-08-24
Kevin P Murphy
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 A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

 Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.

 The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

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 0262018020
 2. ISBN-13
 978-0262018029
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 Illustrated
 4. Publisher
 The MIT Press
 5. Publication date
 August 24, 2012
 6. Language
 English
 7. Dimensions
 8.25 x 1.79 x 9.27 inches
 8. Print length
 1104 pages
 9. See all details
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 Editorial Reviews

 Review

 This comprehensive book should be of great interest to learners and practitioners in the field of machine learning.— British Computer Society —

 About the Author

 Kevin P. Murphy is a Senior Staff Research Scientist at Google Research.

 Product details

 * Publisher ‏ : ‎ The MIT Press; Illustrated edition (August 24, 2012)
 * Language ‏ : ‎ English
 * Hardcover ‏ : ‎ 1104 pages
 * ISBN-10 ‏ : ‎ 0262018020
 * ISBN-13 ‏ : ‎ 978-0262018029
 * Item Weight ‏ : ‎ 2.31 pounds
 * Dimensions ‏ : ‎ 8.25 x 1.79 x 9.27 inches
 * Best Sellers Rank: #120,037 in Books ( See Top 100 in Books )
 + #16 in Machine Theory (Books)
 + #207 in Core
 + #238 in Artificial Intelligence & Semantics
 * Customer Reviews:
 4.4 4.4 out of 5 stars 338 ratings
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 Kevin P. Murphy

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 Kevin Patrick Murphy was born in Ireland, grew up in England (BA from Cambridge),

 and went to graduate school in the USA (MEng from U. Penn, PhD from UC Berkeley,

 Postdoc at MIT). In 2004, he became a professor of computer science and statistics

 at the University of British Columbia in Vancouver, Canada. In 2011, he went to

 Google in Mountain View, California for his sabbatical. In 2012, he

 converted to a full-time research scientist position at Google. Kevin has

 published over 50 papers in refereed conferences and journals related

 to machine learning and graphical models. He has recently published

 an 1100-page textbook called "Machine Learning: a Probabilistic

 Perspective" (MIT Press, 2012).

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  5 Stars
  Great book for ML postgraduate students
  I got the book as a gift for Christmas. I’ve been skimming through the book at the moment, but I love the range of ML topics it addresses, however the depth in certain areas, it can be a great reference book or a great starting guide.
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  5.0 out of 5 stars Great book
  Reviewed in the United States on June 5, 2018
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  I love this book. It's cutting-edge and comprehensive as nothing else on the market and has the right mathematical level (for me, at any rate; I find this to be about right, and Hastie et al to be a bit hard). It makes a great desk reference for day-to-day work.

  Speaking of comprehensiveness, did you want to know about a symmetric version of KL divergence? It's there in 2.8.2. How about dynamic latent dirichlet allocation? See 27.4.2. Examples from cognitive science? See his refs to Tenenbaum and Xu's work. How about a reference to the fact that consistent preferences allow one to represent them as utilities? See 5.7.3.2. The discussion on utilities is brief, but I appreciate this sort of connection-making between disciplines (ML and micro-economics, in this case), even if cursory. Please note: I don't mean to suggest that his comprehensiveness is merely a question of making short references, rather that over and above all the deep & detailed material he presents he still finds the time/space to add these little cross-connections that, to me, make the book a lot more valuable.

  Minor complaints: I wonder why he didn't give much attention to newer neural network architectures (e.g. lstm). It also seems a bit weird that he seems to think of Bayesianism as the "one true way", and I say this as someone who loves the Bayesian philosophy. These are minor quibbles.
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  5.0 out of 5 stars This is certainly the best book on machine learning.
  Reviewed in the United States on June 5, 2021
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  Just compare the section on linear regression of this book to any other book and you will see why it's the bible of machine learning and the most important book to read in this field. Nevertheless, it requires quite a bit background in estimation theory.
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  world citizen
  5.0 out of 5 stars One of the best books on probabilistic approaches to machine learning
  Reviewed in the United States on October 26, 2014
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  One of the best books on probabilistic approaches to machine learning. I like that the author not only describes various techniques but also takes the time to try to pull out the intuitions behind concepts, compares techniques to help the reader understand what makes a technique special and provides a detailed bibliography so that details can be followed up in the original literature. The book covers a huge swath of material from basic theory of Bayesian reasoning all the way to recent work in deep learning models. I find I learn something new every time I pick it up. The first edition had a lot of errors, but there are published errata and I understand that later editions have been fixed.
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  Panos Lambrianides
  4.0 out of 5 stars Great book as a supplement
  Reviewed in the United States on July 11, 2017
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  This book is a bit of a mixed bag. It is more current than Bishop's book and on paper more complete, but the writing quality varies from chapter to chapter. I think it is best used as a complement to good lecture notes as it is more encyclopedic in nature. The didactic quality is great in some chapters, especially the early ones, but skimpy in the more advanced topics. A great positive of this book is the accompanying software is readily available online.

  On the whole a great book to have on your shelf if you are serious about AI, but not on its own. As far as the typos, they are pretty easy to spot so they were not a concern.
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  Kelly R. Boswell
  5.0 out of 5 stars Pattern Recognition and Machine Learning (Bishop) is also a great book. I also found some videos made by ...
  Reviewed in the United States on July 31, 2017
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  I'd think of this book as a reference book for the field of Machine Learning. It has a bit of everything in it but it doesn't have enough room to cover everything in depth, although it does do so for a few topics (Especially Gaussian Models in Chapter 4). You'll need supplementary texts such as BRML (Bayesian Reasoning and Machine Learning; Barber) and Information Theory, Inference, and Learning Algorithms; MacKay), for example. Both of these books are freely distributed in electronic format by their authors. Pattern Recognition and Machine Learning (Bishop) is also a great book. I also found some videos made by Varun Chandola for a ML course at the University of Buffalo on YouTube. Since I'm new to the field, the videos really helped me through the first several chapters, which is all you need. Once you understand the fundamentals, you can read the rest of the chapters without much of a problem. It's still a big effort, though.

  All in all, it's a great book but you have to realize that if the author tried to cover every topic in depth it would be 5 times as big as it already is. So, naturally, some steps in the math are going to be cut out, left as an exercise to the reader, yadda-yadda. I think it's still a 5-star reference book, though.
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  AU_NLPer
  3.0 out of 5 stars Partial review about some details
  Reviewed in the United States on November 8, 2012
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  After I read this thread [...], I decide to buy this MLAPP. I am not working closely on machine learning, but I use machine learning techniques frequently in my research. The review is partial, because I only carefully read some independent parts.

  When decide to buy this book. I care about and expect to obtain information on 1) the appropriateness of when I choose a machine learning method for a task; 2) the big picture of whole machine learning techniques; 3) the intuition of important techniques explained without jargons, and 4) how are these techniques connected;
  Additionally, as an English as a second language (ESL) user, I expect the text is well organised and is written without ambiguity.

  From my partial reading (mainly section 16.6 and some random stuff), I would say the book indeed covers many popular techniques (topic models, deep learnings). The author also did a very good job in describing terminologies and giving succinct descriptions about the key part and the application of each method. These are the two features I like most.

  However, the downsides are 1) it is too abstract for some method descriptions that I need to leverage external materials (e.g. the original paper) to understand the method. Even though the description in the book covers the most important part, it is not sufficient and I would like to see more details. One example is in equation 16.105, why P_b(X) is not explained?
  2) The text is like an oral presentation in a very long tutorial intended to cover an index of techniques. Overall, its readability (text in Section 16.6) dispoints me. I know English is not my native language. But I would still argue for this issue. Previous I read some chapters in Bishop's PRML and the lecture notes of machine learning in coursera, they do better jobs.
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  Giancarlo V.
  5.0 out of 5 stars ottimo!
  Reviewed in Italy on February 9, 2022
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  ottimo!
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  Sooa
  5.0 out of 5 stars Its not a book you want to read from front to back but a great reference. I use it with other books
  Reviewed in Canada on June 29, 2017
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  Bible of Machine learning .

  Its not a book you want to read from front to back but a great reference. I use it with other books.
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  Edward De Brouwer
  5.0 out of 5 stars Great Reference Book
  Reviewed in France on October 13, 2017
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  MLAPP is a good reference book for machine learning for whom want to have mathematical basis for the derivation of state of the art ml methods.
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  Rusty
  5.0 out of 5 stars Very detailled and math intense
  Reviewed in Germany on August 21, 2017
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  This is in my eyes a very solid machine learning book. Due to the publication date it is more up to date than TESL by Trevor, Hastie et.al. However, this book is probably not very appealing for a beginner in the field, due to the math heavy descriptions. If you are more than just practitioner in the field and you are looking for a comprehensive compendium of classical machine learning techniques this might be for you. Note that the current hype topic deep learning is only covered briefly as a curious outlook in one of the later chapters.
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  Maddy
  5.0 out of 5 stars Awesome Book
  Reviewed in India on January 6, 2016
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  Awesome Book. This should really be the First book you read on Machine Learning. The Probabilistic Approach and Heavy Math might tire you at first but if you stay on with the materials and after few revisions, all Algorithms that are prominently used in the Industry today start to make sense readily. Understanding the Math and associating the tuning parameters to the Math allows you to better tune your models.
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 Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) Illustrated Edition
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 A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
 Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.
 The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
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 0262018020
 2. ISBN-13
 978-0262018029
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 Illustrated
 4. Publisher
 The MIT Press
 5. Publication date
 August 24, 2012
 6. Language
 English
 7. Dimensions
 8.25 x 1.79 x 9.27 inches
 8. Print length
 1104 pages
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 Editorial Reviews
 Review
 This comprehensive book should be of great interest to learners and practitioners in the field of machine learning.— British Computer Society —
 About the Author
 Kevin P. Murphy is a Senior Staff Research Scientist at Google Research.
 Product details
 * Publisher ‏ : ‎ The MIT Press; Illustrated edition (August 24, 2012)
 * Language ‏ : ‎ English
 * Hardcover ‏ : ‎ 1104 pages
 * ISBN-10 ‏ : ‎ 0262018020
 * ISBN-13 ‏ : ‎ 978-0262018029
 * Item Weight ‏ : ‎ 2.31 pounds
 * Dimensions ‏ : ‎ 8.25 x 1.79 x 9.27 inches
 * Best Sellers Rank: #120,037 in Books ( See Top 100 in Books )
 + #16 in Machine Theory (Books)
 + #207 in Core
 + #238 in Artificial Intelligence & Semantics
 * Customer Reviews:
 4.4 4.4 out of 5 stars 338 ratings
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 Kevin Patrick Murphy was born in Ireland, grew up in England (BA from Cambridge),
 and went to graduate school in the USA (MEng from U. Penn, PhD from UC Berkeley,
 Postdoc at MIT). In 2004, he became a professor of computer science and statistics
 at the University of British Columbia in Vancouver, Canada. In 2011, he went to
 Google in Mountain View, California for his sabbatical. In 2012, he
 converted to a full-time research scientist position at Google. Kevin has
 published over 50 papers in refereed conferences and journals related
 to machine learning and graphical models. He has recently published
 an 1100-page textbook called "Machine Learning: a Probabilistic
 Perspective" (MIT Press, 2012).
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  5 Stars
  Great book for ML postgraduate students
  I got the book as a gift for Christmas. I’ve been skimming through the book at the moment, but I love the range of ML topics it addresses, however the depth in certain areas, it can be a great reference book or a great starting guide.
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  5.0 out of 5 stars Great book
  Reviewed in the United States on June 5, 2018
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  I love this book. It's cutting-edge and comprehensive as nothing else on the market and has the right mathematical level (for me, at any rate; I find this to be about right, and Hastie et al to be a bit hard). It makes a great desk reference for day-to-day work.
  Speaking of comprehensiveness, did you want to know about a symmetric version of KL divergence? It's there in 2.8.2. How about dynamic latent dirichlet allocation? See 27.4.2. Examples from cognitive science? See his refs to Tenenbaum and Xu's work. How about a reference to the fact that consistent preferences allow one to represent them as utilities? See 5.7.3.2. The discussion on utilities is brief, but I appreciate this sort of connection-making between disciplines (ML and micro-economics, in this case), even if cursory. Please note: I don't mean to suggest that his comprehensiveness is merely a question of making short references, rather that over and above all the deep & detailed material he presents he still finds the time/space to add these little cross-connections that, to me, make the book a lot more valuable.
  Minor complaints: I wonder why he didn't give much attention to newer neural network architectures (e.g. lstm). It also seems a bit weird that he seems to think of Bayesianism as the "one true way", and I say this as someone who loves the Bayesian philosophy. These are minor quibbles.
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  5.0 out of 5 stars This is certainly the best book on machine learning.
  Reviewed in the United States on June 5, 2021
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  Just compare the section on linear regression of this book to any other book and you will see why it's the bible of machine learning and the most important book to read in this field. Nevertheless, it requires quite a bit background in estimation theory.
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  5.0 out of 5 stars One of the best books on probabilistic approaches to machine learning
  Reviewed in the United States on October 26, 2014
  Verified Purchase
  One of the best books on probabilistic approaches to machine learning. I like that the author not only describes various techniques but also takes the time to try to pull out the intuitions behind concepts, compares techniques to help the reader understand what makes a technique special and provides a detailed bibliography so that details can be followed up in the original literature. The book covers a huge swath of material from basic theory of Bayesian reasoning all the way to recent work in deep learning models. I find I learn something new every time I pick it up. The first edition had a lot of errors, but there are published errata and I understand that later editions have been fixed.
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  Panos Lambrianides
  4.0 out of 5 stars Great book as a supplement
  Reviewed in the United States on July 11, 2017
  Verified Purchase
  This book is a bit of a mixed bag. It is more current than Bishop's book and on paper more complete, but the writing quality varies from chapter to chapter. I think it is best used as a complement to good lecture notes as it is more encyclopedic in nature. The didactic quality is great in some chapters, especially the early ones, but skimpy in the more advanced topics. A great positive of this book is the accompanying software is readily available online.
  On the whole a great book to have on your shelf if you are serious about AI, but not on its own. As far as the typos, they are pretty easy to spot so they were not a concern.
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  Kelly R. Boswell
  5.0 out of 5 stars Pattern Recognition and Machine Learning (Bishop) is also a great book. I also found some videos made by ...
  Reviewed in the United States on July 31, 2017
  Verified Purchase
  I'd think of this book as a reference book for the field of Machine Learning. It has a bit of everything in it but it doesn't have enough room to cover everything in depth, although it does do so for a few topics (Especially Gaussian Models in Chapter 4). You'll need supplementary texts such as BRML (Bayesian Reasoning and Machine Learning; Barber) and Information Theory, Inference, and Learning Algorithms; MacKay), for example. Both of these books are freely distributed in electronic format by their authors. Pattern Recognition and Machine Learning (Bishop) is also a great book. I also found some videos made by Varun Chandola for a ML course at the University of Buffalo on YouTube. Since I'm new to the field, the videos really helped me through the first several chapters, which is all you need. Once you understand the fundamentals, you can read the rest of the chapters without much of a problem. It's still a big effort, though.
  All in all, it's a great book but you have to realize that if the author tried to cover every topic in depth it would be 5 times as big as it already is. So, naturally, some steps in the math are going to be cut out, left as an exercise to the reader, yadda-yadda. I think it's still a 5-star reference book, though.
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  AU_NLPer
  3.0 out of 5 stars Partial review about some details
  Reviewed in the United States on November 8, 2012
  Verified Purchase
  After I read this thread [...], I decide to buy this MLAPP. I am not working closely on machine learning, but I use machine learning techniques frequently in my research. The review is partial, because I only carefully read some independent parts.
  When decide to buy this book. I care about and expect to obtain information on 1) the appropriateness of when I choose a machine learning method for a task; 2) the big picture of whole machine learning techniques; 3) the intuition of important techniques explained without jargons, and 4) how are these techniques connected;
  Additionally, as an English as a second language (ESL) user, I expect the text is well organised and is written without ambiguity.
  From my partial reading (mainly section 16.6 and some random stuff), I would say the book indeed covers many popular techniques (topic models, deep learnings). The author also did a very good job in describing terminologies and giving succinct descriptions about the key part and the application of each method. These are the two features I like most.
  However, the downsides are 1) it is too abstract for some method descriptions that I need to leverage external materials (e.g. the original paper) to understand the method. Even though the description in the book covers the most important part, it is not sufficient and I would like to see more details. One example is in equation 16.105, why P_b(X) is not explained?
  2) The text is like an oral presentation in a very long tutorial intended to cover an index of techniques. Overall, its readability (text in Section 16.6) dispoints me. I know English is not my native language. But I would still argue for this issue. Previous I read some chapters in Bishop's PRML and the lecture notes of machine learning in coursera, they do better jobs.
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  Giancarlo V.
  5.0 out of 5 stars ottimo!
  Reviewed in Italy on February 9, 2022
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  ottimo!
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  Sooa
  5.0 out of 5 stars Its not a book you want to read from front to back but a great reference. I use it with other books
  Reviewed in Canada on June 29, 2017
  Verified Purchase
  Bible of Machine learning .
  Its not a book you want to read from front to back but a great reference. I use it with other books.
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  Edward De Brouwer
  5.0 out of 5 stars Great Reference Book
  Reviewed in France on October 13, 2017
  Verified Purchase
  MLAPP is a good reference book for machine learning for whom want to have mathematical basis for the derivation of state of the art ml methods.
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  Rusty
  5.0 out of 5 stars Very detailled and math intense
  Reviewed in Germany on August 21, 2017
  Verified Purchase
  This is in my eyes a very solid machine learning book. Due to the publication date it is more up to date than TESL by Trevor, Hastie et.al. However, this book is probably not very appealing for a beginner in the field, due to the math heavy descriptions. If you are more than just practitioner in the field and you are looking for a comprehensive compendium of classical machine learning techniques this might be for you. Note that the current hype topic deep learning is only covered briefly as a curious outlook in one of the later chapters.
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  Maddy
  5.0 out of 5 stars Awesome Book
  Reviewed in India on January 6, 2016
  Verified Purchase
  Awesome Book. This should really be the First book you read on Machine Learning. The Probabilistic Approach and Heavy Math might tire you at first but if you stay on with the materials and after few revisions, all Algorithms that are prominently used in the Industry today start to make sense readily. Understanding the Math and associating the tuning parameters to the Math allows you to better tune your models.
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Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series): Murphy, Kevin P.: 9780262018029: Amazon.com: Books
https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020?tag=hackr-20&geniuslink=true
https://www.deeplearningbook.org/
Deep Learning
2016-01-01
None
<div>
 
 
 <h2>An MIT Press book</h2>
 <h3>Ian Goodfellow and Yoshua Bengio and Aaron Courville</h3>
 
<p>
 
 <a href="exercises.html">Exercises</a>  
 <a href="lecture_slides.html">Lectures</a>  
 <a href="external.html">External Links</a>  
</p>
<p>
The Deep Learning textbook is a resource intended to help students
and practitioners enter the field of machine learning in general
and deep learning in particular.
The online version of the book is now complete and will remain
available online for free.
</p>
<p>The deep learning textbook can now be ordered on
<a href="https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/ref=sr_1_1?ie=UTF8&amp;qid=1472485235&amp;sr=8-1&amp;keywords=deep+learning+book">Amazon</a>.
</p><p>For up to date announcements, join our
<a href="https://groups.google.com/forum/#!forum/deeplearningbook">mailing list</a>.
</p>
 <h2>Citing the book</h2>
 To cite this book, please use this bibtex entry:
<pre>@book{Goodfellow-et-al-2016,
 title={Deep Learning},
 author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
 publisher={MIT Press},
 note={\url{http://www.deeplearningbook.org}},
 year={2016}
}
</pre>
<p>
To write your own document using our LaTeX style, math notation, or
to copy our notation page, download our
<a href="https://github.com/goodfeli/dlbook_notation">template</a> files.
</p>
<p>
<a href="https://docs.google.com/document/d/1ABlp7FluwZ0B82_fjNOFVQ2uOZkfuF8elbofhZmNXag/edit?usp=sharing">Errata in published editions</a>
</p>
<h2>Deep Learning</h2>
 <ul>
 <li> <a href="contents/TOC.html">Table of Contents</a></li>
 <li><a href="contents/acknowledgements.html">Acknowledgements</a></li>
 <li><a href="contents/notation.html">Notation</a></li>
 <li> <a href="contents/intro.html">1 Introduction</a></li>
 <li> <a href="contents/part_basics.html">Part I: Applied Math and Machine Learning Basics</a></li>
 <ul>
 <li> <a href="contents/linear_algebra.html">2 Linear Algebra</a></li>
 <li> <a href="contents/prob.html">3 Probability and Information Theory</a></li>
 <li> <a href="contents/numerical.html">4 Numerical Computation</a></li>
 <li> <a href="contents/ml.html">5 Machine Learning Basics</a></li>
 </ul>
 <li> <a href="contents/part_practical.html">Part II: Modern Practical Deep Networks</a></li>
 <ul>
 <li> <a href="contents/mlp.html">6 Deep Feedforward Networks</a></li>
 <li> <a href="contents/regularization.html">7 Regularization for Deep Learning</a></li>
 <li> <a href="contents/optimization.html">8 Optimization for Training Deep Models</a></li>
 <li> <a href="contents/convnets.html">9 Convolutional Networks</a></li>
 <li> <a href="contents/rnn.html">10 Sequence Modeling: Recurrent and Recursive Nets</a></li>
 <li> <a href="contents/guidelines.html">11 Practical Methodology</a></li>
 <li> <a href="contents/applications.html">12 Applications</a></li>
 </ul>
 <li> <a href="contents/part_research.html">Part III: Deep Learning Research</a></li>
 <ul>
 <li> <a href="contents/linear_factors.html">13 Linear Factor Models</a></li>
 <li> <a href="contents/autoencoders.html">14 Autoencoders</a></li>
 <li> <a href="contents/representation.html">15 Representation Learning</a></li>
 <li> <a href="contents/graphical_models.html">16 Structured Probabilistic Models for Deep Learning</a></li>
 <li> <a href="contents/monte_carlo.html">17 Monte Carlo Methods</a></li>
 <li> <a href="contents/partition.html">18 Confronting the Partition Function</a></li>
 <li> <a href="contents/inference.html">19 Approximate Inference</a></li>
 <li> <a href="contents/generative_models.html">20 Deep Generative Models</a></li>
 </ul>
 </ul>
 <li> <a href="contents/bib.html">Bibliography</a></li>
 <li> <a href="contents/index-.html">Index</a></li>
 
 
<h2>FAQ</h2>
<ul>
<li>Can I get a PDF of this book?
<p>No, our contract with MIT Press forbids distribution of too easily copied
electronic formats of the book.
</p>
</li>
<li>Why are you using HTML format for the web version of the book?
<p>This format is a sort of weak DRM required by our contract with MIT Press.
It's intended to discourage unauthorized copying/editing
of the book. 
</p></li>
<li>What is the best way to print the HTML format?
<p>Printing seems to work best printing directly from the browser, using Chrome.
Other browsers do not work as well.
</p>
</li>
<li>Can I translate the book into Chinese?</li>
<p>Posts and Telecom Press has purchased the rights.
</p>
</ul>
<p>
If you notice any typos (besides the known issues listed below) or have suggestions for exercises to add to the
website, do not hesitate to contact the authors directly by e-mail
at: feedback@deeplearningbook.org 
</p>
<p>
Since the book is complete and in print, we do not make large changes,
only small corrections.
</p>
<p>Known issues: In outdated versions of the Edge
browser, the "does not equal" sign sometimes appears as the "equals" sign.
This may be resolved by updating to the latest version.
</p>
 
||||I||||Deep Learning

An MIT Press book

Ian Goodfellow and Yoshua Bengio and Aaron Courville


 Exercises   Lectures   External Links  

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.

The deep learning textbook can now be ordered on Amazon.

For up to date announcements, join our mailing list.

Citing the book

To cite this book, please use this bibtex entry:

@book{Goodfellow-et-al-2016,
 title={Deep Learning},
 author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
 publisher={MIT Press},
 note={\url{http://www.deeplearningbook.org}},
 year={2016}
}


To write your own document using our LaTeX style, math notation, or to copy our notation page, download our template files.

Errata in published editions

Deep Learning

 * Table of Contents
 * Acknowledgements
 * Notation
 * 1 Introduction
 * Part I: Applied Math and Machine Learning Basics
 + 2 Linear Algebra
 + 3 Probability and Information Theory
 + 4 Numerical Computation
 + 5 Machine Learning Basics
 * Part II: Modern Practical Deep Networks
 + 6 Deep Feedforward Networks
 + 7 Regularization for Deep Learning
 + 8 Optimization for Training Deep Models
 + 9 Convolutional Networks
 + 10 Sequence Modeling: Recurrent and Recursive Nets
 + 11 Practical Methodology
 + 12 Applications
 * Part III: Deep Learning Research
 + 13 Linear Factor Models
 + 14 Autoencoders
 + 15 Representation Learning
 + 16 Structured Probabilistic Models for Deep Learning
 + 17 Monte Carlo Methods
 + 18 Confronting the Partition Function
 + 19 Approximate Inference
 + 20 Deep Generative Models
* Bibliography
* Index


FAQ

 * Can I get a PDF of this book?

 No, our contract with MIT Press forbids distribution of too easily copied electronic formats of the book.

 * Why are you using HTML format for the web version of the book?

 This format is a sort of weak DRM required by our contract with MIT Press. It's intended to discourage unauthorized copying/editing of the book.

 * What is the best way to print the HTML format?

 Printing seems to work best printing directly from the browser, using Chrome. Other browsers do not work as well.

 * Can I translate the book into Chinese?

 Posts and Telecom Press has purchased the rights.

If you notice any typos (besides the known issues listed below) or have suggestions for exercises to add to the website, do not hesitate to contact the authors directly by e-mail at: feedback@deeplearningbook.org

Since the book is complete and in print, we do not make large changes, only small corrections.

Known issues: In outdated versions of the Edge browser, the "does not equal" sign sometimes appears as the "equals" sign. This may be resolved by updating to the latest version.

An MIT Press book
 Ian Goodfellow and Yoshua Bengio and Aaron Courville
 
 
 Exercises  
 Lectures  
 External Links  
The Deep Learning textbook is a resource intended to help students
and practitioners enter the field of machine learning in general
and deep learning in particular.
The online version of the book is now complete and will remain
available online for free.
The deep learning textbook can now be ordered on
Amazon.
 For up to date announcements, join our
mailing list.
 Citing the book
 To cite this book, please use this bibtex entry:
@book{Goodfellow-et-al-2016,
 title={Deep Learning},
 author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
 publisher={MIT Press},
 note={\url{http://www.deeplearningbook.org}},
 year={2016}
}
To write your own document using our LaTeX style, math notation, or
to copy our notation page, download our
template files.
Errata in published editions
Deep Learning
 
  Table of Contents 
  Acknowledgements 
  Notation 
  1 Introduction 
  Part I: Applied Math and Machine Learning Basics 
 
  2 Linear Algebra 
  3 Probability and Information Theory 
  4 Numerical Computation 
  5 Machine Learning Basics 
 
  Part II: Modern Practical Deep Networks 
 
  6 Deep Feedforward Networks 
  7 Regularization for Deep Learning 
  8 Optimization for Training Deep Models 
  9 Convolutional Networks 
  10 Sequence Modeling: Recurrent and Recursive Nets 
  11 Practical Methodology 
  12 Applications 
 
  Part III: Deep Learning Research 
 
  13 Linear Factor Models 
  14 Autoencoders 
  15 Representation Learning 
  16 Structured Probabilistic Models for Deep Learning 
  17 Monte Carlo Methods 
  18 Confronting the Partition Function 
  19 Approximate Inference 
  20 Deep Generative Models 
 
 
  Bibliography 
  Index 
 
 
FAQ
Can I get a PDF of this book?
No, our contract with MIT Press forbids distribution of too easily copied
electronic formats of the book.
Why are you using HTML format for the web version of the book?
This format is a sort of weak DRM required by our contract with MIT Press.
It's intended to discourage unauthorized copying/editing
of the book. 
 
What is the best way to print the HTML format?
Printing seems to work best printing directly from the browser, using Chrome.
Other browsers do not work as well.
Can I translate the book into Chinese?
Posts and Telecom Press has purchased the rights.
If you notice any typos (besides the known issues listed below) or have suggestions for exercises to add to the
website, do not hesitate to contact the authors directly by e-mail
at: feedback@deeplearningbook.org 
Since the book is complete and in print, we do not make large changes,
only small corrections.
Known issues: In outdated versions of the Edge
browser, the "does not equal" sign sometimes appears as the "equals" sign.
This may be resolved by updating to the latest version.
Deep Learning
https://www.deeplearningbook.org/