Advanced machine learning books
https://probml.github.io/pml-book/book2.html
Probabilistic Machine Learning: Advanced Topics
2022-02-28
None
<div>
by <a href="https://www.cs.ubc.ca/~murphyk/">Kevin Patrick Murphy</a>.
<br />
MIT Press, 2023.
	<p>
	 
</p><h2>Key links</h2>
<ul>
	 
	<li> <a href="#toc">Short table of contents</a>
	 </li><li> <a href="https://github.com/probml/pml2-book/blob/main/toc2-long-2023-01-19.pdf">
	 Long table of contents</a>
	 
</li><li> <a href="https://github.com/probml/pml2-book/blob/main/preface2-2023-01-02.pdf">Preface</a>
 </li><li> <a href="https://github.com/probml/pml2-book/releases/latest/download/book2.pdf">
 Draft pdf of the main book</a>, 2023-04-01. CC-BY-NC-ND license. (Please cite the official reference below.)
 	 
</li><li> <a href="https://github.com/probml/pml-book/blob/main/supp2.md">Supplementary material</a>
	
 </li><li> <a href="https://github.com/probml/pml2-book/issues">Issue tracker</a>. 
	 
	 </li><li> <a href="https://github.com/probml/pyprobml/tree/master/notebooks/book2">
	 Code to reproduce most of the figures</a> 
	 
	 
</li><li> <a href="#ack">Acknowledgements</a>
	</li><li> <a href="#endorsements">Endorsements</a>
	
</li></ul>
If you use this book, please be sure to cite
<pre><code>
 @book{pml2Book,
 author = "Kevin P. Murphy",
 title = "Probabilistic Machine Learning: Advanced Topics",
 publisher = "MIT Press",
 year = 2023,
 url = "http://probml.github.io/book2"
}
</code></pre>
<p> Downloads since 2022-02-28. 
 
 
</p><h2><a>Table of contents</a></h2><a>
 
 
	 
</a><h2><a></a><a></a>Endorsements</h2>
<ul>
	<li>
	"Kevin Murphy has distilled the vast and confusing literature on machine learning and neural networks
	into a beautifully written 
	and extremely clear textbook that will be a wonderful resource for both new students 
	and seasoned researchers who are trying to keep up with this fast moving field. 
	The chapter on generative models is a masterpiece."
	-- <a href="https://www.cs.toronto.edu/~hinton/">Geoff Hinton</a>, U. Toronto/ Google.
	 
	</li><li> "Kevin Murphy had already impressed and greatly benefited the machine learning community with his introductory
	book on probabilistic ML
	and I am delighted to see the depth and breadth of material in his new sequel on advanced probabilistic ML.
	The book covers topics which I believe are at the heart of past and upcoming advances in our field,
	while often lacking in the training of graduate students in computer science, 
	and I therefore recommend it highly to all of them."
	-- <a href="https://yoshuabengio.org/">Yoshua Bengio</a>, U. Montreal
	</li><li> "This book is an amazing tour de force: Murphy and his co-authors have described and systematized virtually 
	all of the important advances in machine learning over the past 30 years. Pick any topic, and they provide a 
	crisp description of the state-of-the-art methods in a common, well-chosen notation and using a set of core concepts 
	in modeling, statistics, and optimization. This book will be a valuable starting point for students entering 
	the field and a wonderful reference for seasoned researchers. It is hard to imagine a better antidote to the vast, 
	confusing, and voluminous literature in machine learning. This is such an amazing book! I learned things even in the sections
	where I’m fairly up to date with the literature. " --- <a href="https://web.engr.oregonstate.edu/~tgd/">Tom Dietterich</a>,
	Oregon State University
	</li><li> "The prior version of Dr. Murphy's book was amongst the 3-4 books I recommended to students and machine learning colleagues 
	at all levels as being essential to own, and, perhaps more importantly, to always have readily at hand. 
	As machine learning has matured and evolved, no other comprehensive resource of this nature has even remotely
	kept pace with modern methodological developments. This new version is a blessing for the machine learning community and frankly, 
	at this moment in time, is the only truly necessary machine learning book to own. 
	Very few people in the world can do what Dr. Murphy has done here and the world owes him its thanks." 
	-- <a href="https://www.cs.ubc.ca/~fwood/">Frank Wood</a>, UBC. 
	</li><li> "Whether teaching machine learning to undergrads, master students, or PhD students, 
	I found myself time and time again choosing the 2012 "Machine Learning: A Probabilistic Perspective" as the primary textbook. 
	When I heard about the new "Probabilistic Machine Learning" series, I was thrilled to see the expanded and modernized set of topics;
	this will be the go-to book for my ML courses at Stanford. Kevin Murphy has a phenomenal ability to go deep while making topics 
	digestible to a broad audience. His writing is clear and concise with great visuals throughout. I highly recommend this as "the book" 
	for anyone wanting to become a well-versed ML expert." -- <a href="https://emilybfox.su.domains/">Emily Fox</a>, Stanford.
	</li><li> "Murphy’s book is certainly the most comprehensive resource on machine learning available today. 
	With the growing body of research in the field, 
	it is a daunting challenge to provide an organized perspective of the current state of knowledge. 
	This book achieves this feat by integrating classic material, like MCMC inference, 
	with very recent developments like denoising diffusion models. 
	The material is organized and presented in a very accessible and intuitive manner,
	making the book an asset for any researcher or practitioner in the field." --
	<a href="https://cs3801.wixsite.com/amirgloberson">Amir Globerson</a>, Tel Aviv University.
	</li><li> "This new Advanced Topics volume will provide a crucial path from the foundations of machine
	learning to state-of-the-art probabilistic models, many of which have only been described in the research literature. 
	I particularly like the way it draws parallels between variational and Monte Carlo inference,
	and between graphical models and (Bayesian) deep learning, so one can see the deep links between 
	methods that are sometimes cast as competitors. 
	I look forward to the next generations of probabilistic machine learning that this volume inspires." 
	--- <a href="https://www.ics.uci.edu/~sudderth/">Erik Sudderth</a>, UC Irvine.
	</li><li> "This book provides an outstanding and deep tour of the most foundational ideas in probabilistic machine learning. 
	It explains the essential mathematical and computational tools that a student needs to move beyond the basics. 
	With this book, 
	Murphy provides a comprehensive resource that will be great not just to learn from, but also as a reference 
	to return to again and again." -- <a href="https://www.cs.princeton.edu/~rpa/">Ryan Adams</a>, Princeton.
	</li><li> "Kevin Murphy's book is a landmark achievement in machine learning. It provides an in-depth coverage of
	a wide range of topics in probabilistic machine learning, from inference methods to generative models
	and decision making. It gives a modern perspective on these topics, bringing them up to date with recent
	advances in deep learning and representation learning. The insights in this 
	book are essential for a solid understanding of the field, 
	and I highly recommend it to students and experts alike." 
	-- <a href="http://mlg.eng.cam.ac.uk/zoubin/">Zoubin Ghahramani</a>, Cambridge/Google.
	</li><li> "As a
researcher trained by Murphy's 2012 book, I was excited to read
its sequel "Probabilistic Machine Learning: Advanced Topics." This new
book brings us to the forefront of cutting-edge probabilistic ML,
distilling the recent advances into a systemic exposition. It
communicates the core ideas of these recent advances in an
impressively clear and intuitive way, while managing to convey the
depth of these ideas by situating them in a broad context. It will 
	become a major reference that I constantly return to."
	-- <a href="https://yixinwang.github.io/">Yixin Wang</a>, U. Michigan.
	</li></ul>
	
 <h2><a></a>Acknowledgements</h2>
I would like to thank the following people for helping with this book.
<ul>
<li> People who helped to write various sections and chapters:
	Alex Alemi (Google),
 Jeff Bilmes (U. Washington), 
	Peter Chang,
 Marco Cuturi (Apple, work done at Google), 
	Alexander D'Amour (Google),
	Finale Doshi-Velez (Harvard),
 Roy Frostig (Google), 
	Justin Gilmer (Google),
	Giles Harper-Donnelly,
	Been Kim (Google),
	Durk Kingma (Google),
	Simon Kornblith (Google),
 Balaji Lakshminarayanan (Google), 
	Lihong Li (Amazon, work done at Google), 
 Xinglong Li (UBC),
	Shakir Mohamed (Deepmind),
	George Papamakarios (Deepmind),
	Zeel Patel (IIT Gandhinagar),
	Ben Poole (Google),
	Mihaela Rosca (Deepmind / UCL),
 Vinayak Rao (Purdue), 
	 Yang Song (Stanford),
	Victor Veitch (Google / U. Chicago),
Andrew Wilson (NYU).
	 
 </li><li> Many people who helped make or improve the figures, including:
 Vishal Ghoniya, Ankita Kumari Jain, Aleyna Kara, Zeel Patel, Karm Patel,
 Dhruv Patel, Nitish Sharma, Mahmoud Soliman.
 
</li><li> Participants in the Google Summer of Code (GSOC) for 2021,
including
Ming Liang Ang,
Aleyna Kara, Gerardo Duran-Martin,
Srikar Reddy Jilugu, Drishti Patel,
and co-mentor Mahmoud Soliman.
</li><li> Participants in the Google Summer of Code (GSOC) for 2022,
 including
 Peter Chang, Giles Harper-Donnelly,
 Xinglong Li,
 Zeel Patel, Karm Patel, Qingyao Sun,
and co-mentors Nipun Batra and Scott Linderman.
</li><li> Many other people who contributed code
	(see auto-generated list <a href="https://github.com/probml/pyprobml#acknowledgements">here</a>).
	</li><li> Many people who helped with proof reading (see book preface for list).
	 
 
 </li></ul>
</div>||||I|||| Probabilistic Machine Learning: Advanced Topics

 by Kevin Patrick Murphy.
 MIT Press, 2023.

 Key links

 * Short table of contents
 * Long table of contents
 * Preface
 * Draft pdf of the main book, 2023-04-01. CC-BY-NC-ND license. (Please cite the official reference below.)
 * Supplementary material
 * Issue tracker.
 * Code to reproduce most of the figures
 * Acknowledgements
 * Endorsements
 If you use this book, please be sure to cite
 
 @book{pml2Book,
 author = "Kevin P. Murphy",
 title = "Probabilistic Machine Learning: Advanced Topics",
 publisher = "MIT Press",
 year = 2023,
 url = "http://probml.github.io/book2"
 }
 

 Downloads since 2022-02-28.

 Table of contents

 Endorsements

 * "Kevin Murphy has distilled the vast and confusing literature on machine learning and neural networks into a beautifully written and extremely clear textbook that will be a wonderful resource for both new students and seasoned researchers who are trying to keep up with this fast moving field. The chapter on generative models is a masterpiece." -- Geoff Hinton, U. Toronto/ Google.

 * "Kevin Murphy had already impressed and greatly benefited the machine learning community with his introductory book on probabilistic ML and I am delighted to see the depth and breadth of material in his new sequel on advanced probabilistic ML. The book covers topics which I believe are at the heart of past and upcoming advances in our field, while often lacking in the training of graduate students in computer science, and I therefore recommend it highly to all of them." -- Yoshua Bengio, U. Montreal

 * "This book is an amazing tour de force: Murphy and his co-authors have described and systematized virtually all of the important advances in machine learning over the past 30 years. Pick any topic, and they provide a crisp description of the state-of-the-art methods in a common, well-chosen notation and using a set of core concepts in modeling, statistics, and optimization. This book will be a valuable starting point for students entering the field and a wonderful reference for seasoned researchers. It is hard to imagine a better antidote to the vast, confusing, and voluminous literature in machine learning. This is such an amazing book! I learned things even in the sections where I’m fairly up to date with the literature. " --- Tom Dietterich, Oregon State University

 * "The prior version of Dr. Murphy's book was amongst the 3-4 books I recommended to students and machine learning colleagues at all levels as being essential to own, and, perhaps more importantly, to always have readily at hand. As machine learning has matured and evolved, no other comprehensive resource of this nature has even remotely kept pace with modern methodological developments. This new version is a blessing for the machine learning community and frankly, at this moment in time, is the only truly necessary machine learning book to own. Very few people in the world can do what Dr. Murphy has done here and the world owes him its thanks." -- Frank Wood, UBC.

 * "Whether teaching machine learning to undergrads, master students, or PhD students, I found myself time and time again choosing the 2012 "Machine Learning: A Probabilistic Perspective" as the primary textbook. When I heard about the new "Probabilistic Machine Learning" series, I was thrilled to see the expanded and modernized set of topics; this will be the go-to book for my ML courses at Stanford. Kevin Murphy has a phenomenal ability to go deep while making topics digestible to a broad audience. His writing is clear and concise with great visuals throughout. I highly recommend this as "the book" for anyone wanting to become a well-versed ML expert." -- Emily Fox, Stanford.

 * "Murphy’s book is certainly the most comprehensive resource on machine learning available today. With the growing body of research in the field, it is a daunting challenge to provide an organized perspective of the current state of knowledge. This book achieves this feat by integrating classic material, like MCMC inference, with very recent developments like denoising diffusion models. The material is organized and presented in a very accessible and intuitive manner, making the book an asset for any researcher or practitioner in the field." -- Amir Globerson, Tel Aviv University.

 * "This new Advanced Topics volume will provide a crucial path from the foundations of machine learning to state-of-the-art probabilistic models, many of which have only been described in the research literature. I particularly like the way it draws parallels between variational and Monte Carlo inference, and between graphical models and (Bayesian) deep learning, so one can see the deep links between methods that are sometimes cast as competitors. I look forward to the next generations of probabilistic machine learning that this volume inspires." --- Erik Sudderth, UC Irvine.

 * "This book provides an outstanding and deep tour of the most foundational ideas in probabilistic machine learning. It explains the essential mathematical and computational tools that a student needs to move beyond the basics. With this book, Murphy provides a comprehensive resource that will be great not just to learn from, but also as a reference to return to again and again." -- Ryan Adams, Princeton.

 * "Kevin Murphy's book is a landmark achievement in machine learning. It provides an in-depth coverage of a wide range of topics in probabilistic machine learning, from inference methods to generative models and decision making. It gives a modern perspective on these topics, bringing them up to date with recent advances in deep learning and representation learning. The insights in this book are essential for a solid understanding of the field, and I highly recommend it to students and experts alike." -- Zoubin Ghahramani, Cambridge/Google.

 * "As a researcher trained by Murphy's 2012 book, I was excited to read its sequel "Probabilistic Machine Learning: Advanced Topics." This new book brings us to the forefront of cutting-edge probabilistic ML, distilling the recent advances into a systemic exposition. It communicates the core ideas of these recent advances in an impressively clear and intuitive way, while managing to convey the depth of these ideas by situating them in a broad context. It will become a major reference that I constantly return to." -- Yixin Wang, U. Michigan.

 Acknowledgements

 I would like to thank the following people for helping with this book.
 * People who helped to write various sections and chapters: Alex Alemi (Google), Jeff Bilmes (U. Washington), Peter Chang, Marco Cuturi (Apple, work done at Google), Alexander D'Amour (Google), Finale Doshi-Velez (Harvard), Roy Frostig (Google), Justin Gilmer (Google), Giles Harper-Donnelly, Been Kim (Google), Durk Kingma (Google), Simon Kornblith (Google), Balaji Lakshminarayanan (Google), Lihong Li (Amazon, work done at Google), Xinglong Li (UBC), Shakir Mohamed (Deepmind), George Papamakarios (Deepmind), Zeel Patel (IIT Gandhinagar), Ben Poole (Google), Mihaela Rosca (Deepmind / UCL), Vinayak Rao (Purdue), Yang Song (Stanford), Victor Veitch (Google / U. Chicago), Andrew Wilson (NYU).
 * Many people who helped make or improve the figures, including: Vishal Ghoniya, Ankita Kumari Jain, Aleyna Kara, Zeel Patel, Karm Patel, Dhruv Patel, Nitish Sharma, Mahmoud Soliman.
 * Participants in the Google Summer of Code (GSOC) for 2021, including Ming Liang Ang, Aleyna Kara, Gerardo Duran-Martin, Srikar Reddy Jilugu, Drishti Patel, and co-mentor Mahmoud Soliman.
 * Participants in the Google Summer of Code (GSOC) for 2022, including Peter Chang, Giles Harper-Donnelly, Xinglong Li, Zeel Patel, Karm Patel, Qingyao Sun, and co-mentors Nipun Batra and Scott Linderman.
 * Many other people who contributed code (see auto-generated list here).
 * Many people who helped with proof reading (see book preface for list).
by Kevin Patrick Murphy.
MIT Press, 2023.
	
	 
 Key links
	 
	 Short table of contents
	   
	 Long table of contents
	 
  Preface
   
 Draft pdf of the main book, 2023-04-01. CC-BY-NC-ND license. (Please cite the official reference below.)
 	 
  Supplementary material
	
   Issue tracker. 
	 
	   
	 Code to reproduce most of the figures 
	 
	 
  Acknowledgements
	  Endorsements
	
 
If you use this book, please be sure to cite
 
 @book{pml2Book,
 author = "Kevin P. Murphy",
 title = "Probabilistic Machine Learning: Advanced Topics",
 publisher = "MIT Press",
 year = 2023,
 url = "http://probml.github.io/book2"
}
 
 Downloads since 2022-02-28. 
 
 
  Table of contents  
 
 
	 
     Endorsements
	
	"Kevin Murphy has distilled the vast and confusing literature on machine learning and neural networks
	into a beautifully written 
	and extremely clear textbook that will be a wonderful resource for both new students 
	and seasoned researchers who are trying to keep up with this fast moving field. 
	The chapter on generative models is a masterpiece."
	-- Geoff Hinton, U. Toronto/ Google.
	 
	  "Kevin Murphy had already impressed and greatly benefited the machine learning community with his introductory
	book on probabilistic ML
	and I am delighted to see the depth and breadth of material in his new sequel on advanced probabilistic ML.
	The book covers topics which I believe are at the heart of past and upcoming advances in our field,
	while often lacking in the training of graduate students in computer science, 
	and I therefore recommend it highly to all of them."
	-- Yoshua Bengio, U. Montreal
	  "This book is an amazing tour de force: Murphy and his co-authors have described and systematized virtually 
	all of the important advances in machine learning over the past 30 years. Pick any topic, and they provide a 
	crisp description of the state-of-the-art methods in a common, well-chosen notation and using a set of core concepts 
	in modeling, statistics, and optimization. This book will be a valuable starting point for students entering 
	the field and a wonderful reference for seasoned researchers. It is hard to imagine a better antidote to the vast, 
	confusing, and voluminous literature in machine learning. This is such an amazing book! I learned things even in the sections
	where I’m fairly up to date with the literature. " --- Tom Dietterich,
	Oregon State University
	  "The prior version of Dr. Murphy's book was amongst the 3-4 books I recommended to students and machine learning colleagues 
	at all levels as being essential to own, and, perhaps more importantly, to always have readily at hand. 
	As machine learning has matured and evolved, no other comprehensive resource of this nature has even remotely
	kept pace with modern methodological developments. This new version is a blessing for the machine learning community and frankly, 
	at this moment in time, is the only truly necessary machine learning book to own. 
	Very few people in the world can do what Dr. Murphy has done here and the world owes him its thanks." 
	-- Frank Wood, UBC. 
	  "Whether teaching machine learning to undergrads, master students, or PhD students, 
	I found myself time and time again choosing the 2012 "Machine Learning: A Probabilistic Perspective" as the primary textbook. 
	When I heard about the new "Probabilistic Machine Learning" series, I was thrilled to see the expanded and modernized set of topics;
	this will be the go-to book for my ML courses at Stanford. Kevin Murphy has a phenomenal ability to go deep while making topics 
	digestible to a broad audience. His writing is clear and concise with great visuals throughout. I highly recommend this as "the book" 
	for anyone wanting to become a well-versed ML expert." -- Emily Fox, Stanford.
	  "Murphy’s book is certainly the most comprehensive resource on machine learning available today. 
	With the growing body of research in the field, 
	it is a daunting challenge to provide an organized perspective of the current state of knowledge. 
	This book achieves this feat by integrating classic material, like MCMC inference, 
	with very recent developments like denoising diffusion models. 
	The material is organized and presented in a very accessible and intuitive manner,
	making the book an asset for any researcher or practitioner in the field." --
	Amir Globerson, Tel Aviv University.
	  "This new Advanced Topics volume will provide a crucial path from the foundations of machine
	learning to state-of-the-art probabilistic models, many of which have only been described in the research literature. 
	I particularly like the way it draws parallels between variational and Monte Carlo inference,
	and between graphical models and (Bayesian) deep learning, so one can see the deep links between 
	methods that are sometimes cast as competitors. 
	I look forward to the next generations of probabilistic machine learning that this volume inspires." 
	--- Erik Sudderth, UC Irvine.
	  "This book provides an outstanding and deep tour of the most foundational ideas in probabilistic machine learning. 
	It explains the essential mathematical and computational tools that a student needs to move beyond the basics. 
	With this book, 
	Murphy provides a comprehensive resource that will be great not just to learn from, but also as a reference 
	to return to again and again." -- Ryan Adams, Princeton.
	  "Kevin Murphy's book is a landmark achievement in machine learning. It provides an in-depth coverage of
	a wide range of topics in probabilistic machine learning, from inference methods to generative models
	and decision making. It gives a modern perspective on these topics, bringing them up to date with recent
	advances in deep learning and representation learning. The insights in this 
	book are essential for a solid understanding of the field, 
	and I highly recommend it to students and experts alike." 
	-- Zoubin Ghahramani, Cambridge/Google.
	  "As a
researcher trained by Murphy's 2012 book, I was excited to read
its sequel "Probabilistic Machine Learning: Advanced Topics." This new
book brings us to the forefront of cutting-edge probabilistic ML,
distilling the recent advances into a systemic exposition. It
communicates the core ideas of these recent advances in an
impressively clear and intuitive way, while managing to convey the
depth of these ideas by situating them in a broad context. It will 
	become a major reference that I constantly return to."
	-- Yixin Wang, U. Michigan.
	 
	
   Acknowledgements
I would like to thank the following people for helping with this book.
 People who helped to write various sections and chapters:
	Alex Alemi (Google),
 Jeff Bilmes (U. Washington), 
	Peter Chang,
 Marco Cuturi (Apple, work done at Google), 
	Alexander D'Amour (Google),
	Finale Doshi-Velez (Harvard),
 Roy Frostig (Google), 
	Justin Gilmer (Google),
	Giles Harper-Donnelly,
	Been Kim (Google),
	Durk Kingma (Google),
	Simon Kornblith (Google),
 Balaji Lakshminarayanan (Google), 
	Lihong Li (Amazon, work done at Google), 
 Xinglong Li (UBC),
	Shakir Mohamed (Deepmind),
	George Papamakarios (Deepmind),
	Zeel Patel (IIT Gandhinagar),
	Ben Poole (Google),
	Mihaela Rosca (Deepmind / UCL),
 Vinayak Rao (Purdue), 
	 Yang Song (Stanford),
	Victor Veitch (Google / U. Chicago),
Andrew Wilson (NYU).
	 
   Many people who helped make or improve the figures, including:
 Vishal Ghoniya, Ankita Kumari Jain, Aleyna Kara, Zeel Patel, Karm Patel,
 Dhruv Patel, Nitish Sharma, Mahmoud Soliman.
 
  Participants in the Google Summer of Code (GSOC) for 2021,
including
Ming Liang Ang,
Aleyna Kara, Gerardo Duran-Martin,
Srikar Reddy Jilugu, Drishti Patel,
and co-mentor Mahmoud Soliman.
  Participants in the Google Summer of Code (GSOC) for 2022,
 including
 Peter Chang, Giles Harper-Donnelly,
 Xinglong Li,
 Zeel Patel, Karm Patel, Qingyao Sun,
and co-mentors Nipun Batra and Scott Linderman.
  Many other people who contributed code
	(see auto-generated list here).
	  Many people who helped with proof reading (see book preface for list).
Probabilistic Machine Learning: Advanced Topics
https://probml.github.io/pml-book/book2.html
https://link.springer.com/book/10.1007/978-3-030-70679-1
Variational Methods for Machine Learning with Applications to Deep Networks
2021-05-10
None
<div><div>
<div>
<h2>Overview</h2>
<dl>
<dt>Authors:</dt>
<dd>
<ul>
<li>
<a href="#author-0-1">Matheus Araújo Marins</a>
<sup><a href="#Aff-0-1">1</a></sup>, 
 </li>
<li>
<a href="#author-0-2">Eduardo Antônio Barros da Silva</a>
<sup><a href="#Aff-0-2">2</a></sup>, 
 </li>
<li>…</li>
<li>
<a href="#author-0-3">Sérgio Lima Netto</a>
<sup><a href="#Aff-0-3">3</a></sup>
</li>
</ul>
</dd>
</dl>
<ol>
<li>
<ol>
<li>
<p>
 
 Program of Electrical Engineering - COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil<br/>
</p>
</li>
</ol>
</li>
<li>
<ol>
<li>
<p>
 
 Program of Electrical Engineering - COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil<br/>
</p>
</li>
</ol>
</li>
<li>
<ol>
<li>
<p>
 
 Program of Electrical Engineering - COPPE / Department of Electronics - Poli, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil<br/>
</p>
</li>
</ol>
</li>
<li>
<ol>
<li>
<p>
 
 Program of Electrical Engineering - COPPE / Department of Electronics - Poli, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil<br/>
</p>
</li>
</ol>
</li>
</ol>
<div>
<ul>
<li>Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning</li>
<li>Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes</li>
<li>Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material</li>
</ul>
</div>
<div>
<ul>
<li>
<p>7593 </p>
</li>
<li>
<p>20 </p>
</li>
<li>
<p>3
 </p>
</li>
</ul>
</div>
</div>
<div>
<h2>Access this book</h2>
<p>
<a href="https://wayf.springernature.com?redirect_uri=https%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-3-030-70679-1">
</a>
</p>
<h2>Other ways to access</h2>
</div>
<section>
<h2>Table of contents (5 chapters)</h2>
<section>
<ol>
<li>
</li>
<li>
<div>
<h3>
<a href="https://link.springer.com/chapter/10.1007/978-3-030-70679-1_1">Introduction</a>
</h3>
<ul>
<li>Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antúnio Barros da Silva, Sérgio Lima Netto</li>
</ul>
<p>
</p>
</div>
</li>
<li>
</li>
<li>
</li>
<li>
<div>
<h3>
<a href="https://link.springer.com/chapter/10.1007/978-3-030-70679-1_4">Bayesian Neural Networks</a>
</h3>
<ul>
<li>Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antúnio Barros da Silva, Sérgio Lima Netto</li>
</ul>
<p>
</p>
</div>
</li>
<li>
<div>
<h3>
<a href="https://link.springer.com/chapter/10.1007/978-3-030-70679-1_5">Variational Autoencoder</a>
</h3>
<ul>
<li>Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antúnio Barros da Silva, Sérgio Lima Netto</li>
</ul>
<p>
</p>
</div>
</li>
<li>
</li>
</ol>
<p>
<a href="#back-to-top">
</a>
</p>
</section>
</section>
<section>
<h2>Keywords</h2>
</section>
<section>
<h2>About this book</h2>
<div><p>
 This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The authors present detailed explanations of the main modern algorithms on variational approximations for Bayesian inference in neural networks. Each algorithm of this selected set develops a distinct aspect of the theory. The book builds from the ground-up well-known deep generative models, such as Variational Autoencoder and subsequent theoretical developments. By also exposing the main issues of the algorithms together with different methods to mitigate such issues, the book supplies the necessary knowledge on generative models for the reader to handle a wide range of data types: sequential or not, continuous or not, labelled or not. The book is self-contained, promptly covering all necessary theory so that the reader does not have to search for additional information elsewhere.</p>
<ul><li>Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning;</li><li>Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes;</li><li>Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material.</li></ul>
</div>
</section>
<section>
<h2>Authors and Affiliations</h2>
<div>
<ul>
<li>
<h3>
 Program of Electrical Engineering - COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
 </h3>
<p>
 
 Lucas Pinheiro Cinelli, 
 
 Matheus Araújo Marins
 
 </p>
</li>
<li>
<h3>
 Program of Electrical Engineering - COPPE / Department of Electronics - Poli, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
 </h3>
<p>
 
 Eduardo Antônio Barros da Silva, 
 
 Sérgio Lima Netto
 
 </p>
</li>
</ul>
</div>
</section>
<section>
<h2>About the authors</h2>
<div>
<p>Lucas P. Cinelli was born in Rio de Janeiro, Brazil. He received the Electronics and Computer Engineering degree from the Universidade Federal do Rio de Janeiro (UFRJ), as well as the Engineering degree with major in Electronic Systems, Networks &amp; Images from the Grande École Supélec, in France, due to his academic exchange in 2014-2016. During this period, he also received the Master’s degree in Microtechnologies, Architecture, Communication Networks and Systems from Supélec/INSA-Rennes. In 2019, he received the M.Sc. degree in Electrical Engineering from COPPE/UFRJ, for his dissertation on variational methods for machine learning and is currently pursuing his Ph.D. degree at the same institution. His research on anomaly detection in videos with deep learning alongside his colleagues has led to publications on ICIP 2018 and a Brazilian conference (SBrT) in 2017.</p><p>Matheus A. Marins was born in Rio de Janeiro, Brazil. He received the Electronics and Computer Engineering degree from the Universidade Federal do Rio de Janeiro (UFRJ), in 2016, having done a one-year exchange program at Illinois Institute of Technology (IIT), in the Computer Engineering course. He received the M.Sc. degree in Electrical Engineering from COPPE/UFRJ in 2018, being awarded with a scholarship for his academic performance by the Rio de Janeiro State government. Currently, he is pursuing his Ph.D. degree at the same institution and has shifted his research towards modern Bayesian methods applied to Machine Learning. So far, his research has been focused on Machine Learning, especially on condition-based models to identify and prevent failures on physical systems, which resulted on two international journals (2017 and 2020) and on a Brazilian conference paper (SBrT).</p><p>Eduardo A. B. da Silva was born in Rio de Janeiro, Brazil. He received the Electronics Engineering degree from Instituto Militar de Engenharia (IME), Brazil, in 1984, the M.Sc. degree in Electrical Engineering from Universidade Federal do Rio de Janeiro (COPPE/UFRJ) in 1990, and the Ph.D. degree in Electronics from the University of Essex, England, in 1995.  He is a professor at Universidade Federal do Rio de Janeiro since 1989. He is co-author of the book ”Digital Signal Processing - System Analysis and Design”, published by Cambridge University Press. He published more than 70 papers in international journals.  His research interests lie in the fields of signal and image processing, signal compression, 3D videos, computer vision, light fields and machine learning, together with its applications to telecommunications and the oil and gas industry. He is co-editor of the future standard ISO/IEC CD 21794-2, JPEG Pleno Plenoptic image coding system, and is currently Requirements Vice Chair of JPEG.</p><p>Sergio L. Netto was born in Rio de Janeiro, Brazil. He received the B.Sc. (cum laude) degree from the Universidade Federal do Rio de Janeiro (UFRJ), Brazil, in 1991, the M.Sc. degree from COPPE/UFRJ in 1992, and the Ph.D. degree from the University of Victoria, BC, Canada, in 1996, all in electrical engineering. Since 1997, he has been with the Department of Electronics and Computer Engineering, Poli/UFRJ, and since 1998, he has been with the Program of Electrical Engineering, COPPE/UFRJ. He is the Co-Author (with P. S. R. Diniz and E. A. B. da Silva) of Digital Signal Processing: System Analysis and Design (Cambridge University Press, 2nd edition, 2010), which has also been translated to Chinese and Portuguese. His research and teaching interests lie in the areas of digital signal processing,speech processing, information theory, and computer vision. Prof. Netto received the 2006 Guillemin-Cauer award from the IEEE Circuits and Systems Society for the best paper published in the year of 2005 in the IEEE Trans. Circuits and Systems, Part I: Regular Papers.</p>
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 Variational Methods for Machine Learning with Applications to Deep Networks

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 Authors:
 * Lucas Pinheiro Cinelli 0,
 * Matheus Araújo Marins 1,
 * Eduardo Antônio Barros da Silva 2,
 * …
 * Sérgio Lima Netto 3
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 1. Lucas Pinheiro Cinelli

 1. Program of Electrical Engineering - COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

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 2. Matheus Araújo Marins

 1. Program of Electrical Engineering - COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

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 3. Eduardo Antônio Barros da Silva

 1. Program of Electrical Engineering - COPPE / Department of Electronics - Poli, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

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 4. Sérgio Lima Netto

 1. Program of Electrical Engineering - COPPE / Department of Electronics - Poli, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

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 * Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning
 * Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes
 * Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material

 * 7593 Accesses

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 * 3 Altmetric

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 Table of contents (5 chapters)

 1. Front Matter

 Pages i-xiv
 Download chapter PDF

 2. Introduction

 + Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antúnio Barros da Silva, Sérgio Lima Netto
 Pages 1-4

 3. Fundamentals of Statistical Inference

 + Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antúnio Barros da Silva, Sérgio Lima Netto
 Pages 5-30

 4. Model-Based Machine Learning and Approximate Inference

 + Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antúnio Barros da Silva, Sérgio Lima Netto
 Pages 31-63

 5. Bayesian Neural Networks

 + Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antúnio Barros da Silva, Sérgio Lima Netto
 Pages 65-109

 6. Variational Autoencoder

 + Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antúnio Barros da Silva, Sérgio Lima Netto
 Pages 111-149

 7. Back Matter

 Pages 151-165
 Download chapter PDF
 Back to top

 Keywords

 * Machine theory
 * Artificial intelligence
 * Deep neural networks
 * Bayesian deep learning
 * Bayesian neural network
 * Approximate inference
 * Variational inference
 * Expectation propagation
 * Variational autoencoder
 * Computer vision

 About this book

 This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The authors present detailed explanations of the main modern algorithms on variational approximations for Bayesian inference in neural networks. Each algorithm of this selected set develops a distinct aspect of the theory. The book builds from the ground-up well-known deep generative models, such as Variational Autoencoder and subsequent theoretical developments. By also exposing the main issues of the algorithms together with different methods to mitigate such issues, the book supplies the necessary knowledge on generative models for the reader to handle a wide range of data types: sequential or not, continuous or not, labelled or not. The book is self-contained, promptly covering all necessary theory so that the reader does not have to search for additional information elsewhere.

 * Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning;
 * Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes;
 * Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material.

 Authors and Affiliations

 * Program of Electrical Engineering - COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

 Lucas Pinheiro Cinelli, Matheus Araújo Marins

 * Program of Electrical Engineering - COPPE / Department of Electronics - Poli, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

 Eduardo Antônio Barros da Silva, Sérgio Lima Netto

 About the authors

 Lucas P. Cinelli was born in Rio de Janeiro, Brazil. He received the Electronics and Computer Engineering degree from the Universidade Federal do Rio de Janeiro (UFRJ), as well as the Engineering degree with major in Electronic Systems, Networks & Images from the Grande École Supélec, in France, due to his academic exchange in 2014-2016. During this period, he also received the Master’s degree in Microtechnologies, Architecture, Communication Networks and Systems from Supélec/INSA-Rennes. In 2019, he received the M.Sc. degree in Electrical Engineering from COPPE/UFRJ, for his dissertation on variational methods for machine learning and is currently pursuing his Ph.D. degree at the same institution. His research on anomaly detection in videos with deep learning alongside his colleagues has led to publications on ICIP 2018 and a Brazilian conference (SBrT) in 2017.

 Matheus A. Marins was born in Rio de Janeiro, Brazil. He received the Electronics and Computer Engineering degree from the Universidade Federal do Rio de Janeiro (UFRJ), in 2016, having done a one-year exchange program at Illinois Institute of Technology (IIT), in the Computer Engineering course. He received the M.Sc. degree in Electrical Engineering from COPPE/UFRJ in 2018, being awarded with a scholarship for his academic performance by the Rio de Janeiro State government. Currently, he is pursuing his Ph.D. degree at the same institution and has shifted his research towards modern Bayesian methods applied to Machine Learning. So far, his research has been focused on Machine Learning, especially on condition-based models to identify and prevent failures on physical systems, which resulted on two international journals (2017 and 2020) and on a Brazilian conference paper (SBrT).
 Eduardo A. B. da Silva was born in Rio de Janeiro, Brazil. He received the Electronics Engineering degree from Instituto Militar de Engenharia (IME), Brazil, in 1984, the M.Sc. degree in Electrical Engineering from Universidade Federal do Rio de Janeiro (COPPE/UFRJ) in 1990, and the Ph.D. degree in Electronics from the University of Essex, England, in 1995. He is a professor at Universidade Federal do Rio de Janeiro since 1989. He is co-author of the book ”Digital Signal Processing - System Analysis and Design”, published by Cambridge University Press. He published more than 70 papers in international journals. His research interests lie in the fields of signal and image processing, signal compression, 3D videos, computer vision, light fields and machine learning, together with its applications to telecommunications and the oil and gas industry. He is co-editor of the future standard ISO/IEC CD 21794-2, JPEG Pleno Plenoptic image coding system, and is currently Requirements Vice Chair of JPEG.

 Sergio L. Netto was born in Rio de Janeiro, Brazil. He received the B.Sc. (cum laude) degree from the Universidade Federal do Rio de Janeiro (UFRJ), Brazil, in 1991, the M.Sc. degree from COPPE/UFRJ in 1992, and the Ph.D. degree from the University of Victoria, BC, Canada, in 1996, all in electrical engineering. Since 1997, he has been with the Department of Electronics and Computer Engineering, Poli/UFRJ, and since 1998, he has been with the Program of Electrical Engineering, COPPE/UFRJ. He is the Co-Author (with P. S. R. Diniz and E. A. B. da Silva) of Digital Signal Processing: System Analysis and Design (Cambridge University Press, 2nd edition, 2010), which has also been translated to Chinese and Portuguese. His research and teaching interests lie in the areas of digital signal processing,speech processing, information theory, and computer vision. Prof. Netto received the 2006 Guillemin-Cauer award from the IEEE Circuits and Systems Society for the best paper published in the year of 2005 in the IEEE Trans. Circuits and Systems, Part I: Regular Papers.

 Bibliographic Information

 * Book Title : Variational Methods for Machine Learning with Applications to Deep Networks

 * Authors : Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antônio Barros da Silva, Sérgio Lima Netto

 * DOI : https://doi.org/10.1007/978-3-030-70679-1

 * Publisher : Springer Cham

 * eBook Packages : Engineering , Engineering (R0)

 * Copyright Information : Springer Nature Switzerland AG 2021

 * Hardcover ISBN : 978-3-030-70678-4 Published: 11 May 2021

 * Softcover ISBN : 978-3-030-70681-4 Published: 12 May 2022

 * eBook ISBN : 978-3-030-70679-1 Published: 10 May 2021

 * Edition Number : 1

 * Number of Pages : XIV, 165

 * Number of Illustrations : 21 b/w illustrations, 33 illustrations in colour

 * Topics : Communications Engineering, Networks , Machine Learning , Computational Intelligence , Data Mining and Knowledge Discovery

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Overview
Authors:
Matheus Araújo Marins
 1 , 
 
Eduardo Antônio Barros da Silva
 2 , 
 
…
Sérgio Lima Netto
 3 
 
 Program of Electrical Engineering - COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
 
 Program of Electrical Engineering - COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
 
 Program of Electrical Engineering - COPPE / Department of Electronics - Poli, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
 
 Program of Electrical Engineering - COPPE / Department of Electronics - Poli, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning
Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes
Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material
7593 
20 
3
 
Access this book
Other ways to access
Table of contents (5 chapters)
Introduction
Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antúnio Barros da Silva, Sérgio Lima Netto
Bayesian Neural Networks
Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antúnio Barros da Silva, Sérgio Lima Netto
Variational Autoencoder
Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antúnio Barros da Silva, Sérgio Lima Netto
Keywords
About this book
 
 This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The authors present detailed explanations of the main modern algorithms on variational approximations for Bayesian inference in neural networks. Each algorithm of this selected set develops a distinct aspect of the theory. The book builds from the ground-up well-known deep generative models, such as Variational Autoencoder and subsequent theoretical developments. By also exposing the main issues of the algorithms together with different methods to mitigate such issues, the book supplies the necessary knowledge on generative models for the reader to handle a wide range of data types: sequential or not, continuous or not, labelled or not. The book is self-contained, promptly covering all necessary theory so that the reader does not have to search for additional information elsewhere.
 Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning; Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes; Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material. 
Authors and Affiliations
 Program of Electrical Engineering - COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
 
 
 Lucas Pinheiro Cinelli, 
 
 Matheus Araújo Marins
 
 
 Program of Electrical Engineering - COPPE / Department of Electronics - Poli, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
 
 
 Eduardo Antônio Barros da Silva, 
 
 Sérgio Lima Netto
 
 
About the authors
Lucas P. Cinelli was born in Rio de Janeiro, Brazil. He received the Electronics and Computer Engineering degree from the Universidade Federal do Rio de Janeiro (UFRJ), as well as the Engineering degree with major in Electronic Systems, Networks &amp; Images from the Grande École Supélec, in France, due to his academic exchange in 2014-2016. During this period, he also received the Master’s degree in Microtechnologies, Architecture, Communication Networks and Systems from Supélec/INSA-Rennes. In 2019, he received the M.Sc. degree in Electrical Engineering from COPPE/UFRJ, for his dissertation on variational methods for machine learning and is currently pursuing his Ph.D. degree at the same institution. His research on anomaly detection in videos with deep learning alongside his colleagues has led to publications on ICIP 2018 and a Brazilian conference (SBrT) in 2017. Matheus A. Marins was born in Rio de Janeiro, Brazil. He received the Electronics and Computer Engineering degree from the Universidade Federal do Rio de Janeiro (UFRJ), in 2016, having done a one-year exchange program at Illinois Institute of Technology (IIT), in the Computer Engineering course. He received the M.Sc. degree in Electrical Engineering from COPPE/UFRJ in 2018, being awarded with a scholarship for his academic performance by the Rio de Janeiro State government. Currently, he is pursuing his Ph.D. degree at the same institution and has shifted his research towards modern Bayesian methods applied to Machine Learning. So far, his research has been focused on Machine Learning, especially on condition-based models to identify and prevent failures on physical systems, which resulted on two international journals (2017 and 2020) and on a Brazilian conference paper (SBrT). Eduardo A. B. da Silva was born in Rio de Janeiro, Brazil. He received the Electronics Engineering degree from Instituto Militar de Engenharia (IME), Brazil, in 1984, the M.Sc. degree in Electrical Engineering from Universidade Federal do Rio de Janeiro (COPPE/UFRJ) in 1990, and the Ph.D. degree in Electronics from the University of Essex, England, in 1995.  He is a professor at Universidade Federal do Rio de Janeiro since 1989. He is co-author of the book ”Digital Signal Processing - System Analysis and Design”, published by Cambridge University Press. He published more than 70 papers in international journals.  His research interests lie in the fields of signal and image processing, signal compression, 3D videos, computer vision, light fields and machine learning, together with its applications to telecommunications and the oil and gas industry. He is co-editor of the future standard ISO/IEC CD 21794-2, JPEG Pleno Plenoptic image coding system, and is currently Requirements Vice Chair of JPEG. Sergio L. Netto was born in Rio de Janeiro, Brazil. He received the B.Sc. (cum laude) degree from the Universidade Federal do Rio de Janeiro (UFRJ), Brazil, in 1991, the M.Sc. degree from COPPE/UFRJ in 1992, and the Ph.D. degree from the University of Victoria, BC, Canada, in 1996, all in electrical engineering. Since 1997, he has been with the Department of Electronics and Computer Engineering, Poli/UFRJ, and since 1998, he has been with the Program of Electrical Engineering, COPPE/UFRJ. He is the Co-Author (with P. S. R. Diniz and E. A. B. da Silva) of Digital Signal Processing: System Analysis and Design (Cambridge University Press, 2nd edition, 2010), which has also been translated to Chinese and Portuguese. His research and teaching interests lie in the areas of digital signal processing,speech processing, information theory, and computer vision. Prof. Netto received the 2006 Guillemin-Cauer award from the IEEE Circuits and Systems Society for the best paper published in the year of 2005 in the IEEE Trans. Circuits and Systems, Part I: Regular Papers.
Bibliographic Information
   
   
   
   
 
 
   
   
   
   
   
   
   
 
 
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Variational Methods for Machine Learning with Applications to Deep Networks
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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.

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 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
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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

  4.3 out of 5 stars
  4.3 out of 5
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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.
  Read more
  2 people found this helpful
  Helpful
  Report
  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
  Helpful
  Report
  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
  Helpful
  Report
  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
  Helpful
  Report
  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
  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
  Report
  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.
  Read more
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  Hh
  5.0 out of 5 stars Disappointed
  Reviewed in Germany on November 26, 2019
  Verified Purchase
  Very detailed.
  Read more
  Report
  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
 $20.49
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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
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 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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 1. Machine Learning For Dummies
 John Paul Mueller
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 $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))
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 $23.78 $ 23 . 78
 Get it as soon as Monday, May 6
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 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)
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 4.4 out of 5 stars 1,538
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 $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
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 6. Neural Networks and Deep Learning: A Textbook
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 4.4 out of 5 stars 24
 Hardcover
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 Get it as soon as Monday, May 6
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 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
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  4.3 out of 5
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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
  Helpful
  Report
  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.
  Read more
  2 people found this helpful
  Helpful
  Report
  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
  Helpful
  Report
  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
  Helpful
  Report
  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
  Helpful
  Report
  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.
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  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.
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  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.
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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
  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.
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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
https://github.com/microsoft/ML-For-Beginners
GitHub - microsoft/ML-For-Beginners: 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
2023-05-10
Microsoft
<div><div>
 <article><p><a href="https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE"></a>
<a href="https://github.com/microsoft/ML-For-Beginners/graphs/contributors/"></a>
<a href="https://github.com/microsoft/ML-For-Beginners/issues/"></a>
<a href="https://github.com/microsoft/ML-For-Beginners/pulls/"></a>
<a href="http://makeapullrequest.com/"></a></p>
<p><a href="https://github.com/microsoft/ML-For-Beginners/watchers/"></a>
<a href="https://github.com/microsoft/ML-For-Beginners/network/"></a>
<a href="https://github.com/microsoft/ML-For-Beginners/stargazers/"></a></p>
<h2>Machine Learning for Beginners - A Curriculum</h2>
<blockquote>
<p>🌍 Travel around the world as we explore Machine Learning by means of world cultures 🌍</p>
</blockquote>
<p>Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about <strong>Machine Learning</strong>. In this curriculum, you will learn about what is sometimes called <strong>classic machine learning</strong>, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. Pair these lessons with our <a href="https://aka.ms/datascience-beginners">'Data Science for Beginners' curriculum</a>, as well!</p>
<p>Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment, and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.</p>
<p><strong>✍️ Hearty thanks to our authors</strong> Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, and Amy Boyd</p>
<p><strong>🎨 Thanks as well to our illustrators</strong> Tomomi Imura, Dasani Madipalli, and Jen Looper</p>
<p><strong>🙏 Special thanks 🙏 to our Microsoft Student Ambassador authors, reviewers, and content contributors</strong>, notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal</p>
<p><strong>🤩 Extra gratitude to Microsoft Student Ambassador Eric Wanjau for our R lessons!</strong></p>
<hr />
<h2>Getting Started</h2>
<p><strong><a href="https://aka.ms/student-page">Students</a></strong>, to use this curriculum, fork the entire repo to your own GitHub account and complete the exercises on your own or with a group:</p>
<ul>
<li>Start with a pre-lecture quiz.</li>
<li>Read the lecture and complete the activities, pausing and reflecting at each knowledge check.</li>
<li>Try to create the projects by comprehending the lessons rather than running the solution code; however that code is available in the <code>/solution</code> folders in each project-oriented lesson.</li>
<li>Take the post-lecture quiz.</li>
<li>Complete the challenge.</li>
<li>Complete the assignment.</li>
<li>After completing a lesson group, visit the <a href="https://github.com/microsoft/ML-For-Beginners/discussions">Discussion Board</a> and "learn out loud" by filling out the appropriate PAT rubric. A 'PAT' is a Progress Assessment Tool that is a rubric you fill out to further your learning. You can also react to other PATs so we can learn together.</li>
</ul>
<blockquote>
<p>For further study, we recommend following these <a href="https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott">Microsoft Learn</a> modules and learning paths.</p>
</blockquote>
<p><strong>Teachers</strong>, we have <a href="/microsoft/ML-For-Beginners/blob/main/for-teachers.md">included some suggestions</a> on how to use this curriculum.</p>
<hr />
<h2>Video walkthroughs</h2>
<p>Some of the lessons are available as short form video. You can find all these in-line in the lessons, or on the <a href="https://aka.ms/ml-beginners-videos">ML for Beginners playlist on the Microsoft Developer YouTube channel</a> by clicking the image below.</p>
<p><a href="https://aka.ms/ml-beginners-videos"></a></p>
<hr />
<h2>Meet the Team</h2>
<p><a href="https://youtu.be/Tj1XWrDSYJU"></a></p>
<p><strong>Gif by</strong> <a href="https://linkedin.com/in/mohitjaisal">Mohit Jaisal</a></p>
<blockquote>
<p>🎥 Click the image above for a video about the project and the folks who created it!</p>
</blockquote>
<hr />
<h2>Pedagogy</h2>
<p>We have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on <strong>project-based</strong> and that it includes <strong>frequent quizzes</strong>. In addition, this curriculum has a common <strong>theme</strong> to give it cohesion.</p>
<p>By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12-week cycle. This curriculum also includes a postscript on real-world applications of ML, which can be used as extra credit or as a basis for discussion.</p>
<blockquote>
<p>Find our <a href="/microsoft/ML-For-Beginners/blob/main/CODE_OF_CONDUCT.md">Code of Conduct</a>, <a href="/microsoft/ML-For-Beginners/blob/main/CONTRIBUTING.md">Contributing</a>, and <a href="/microsoft/ML-For-Beginners/blob/main/TRANSLATIONS.md">Translation</a> guidelines. We welcome your constructive feedback!</p>
</blockquote>
<h2>Each lesson includes:</h2>
<ul>
<li>optional sketchnote</li>
<li>optional supplemental video</li>
<li>video walkthrough (some lessons only)</li>
<li>pre-lecture warmup quiz</li>
<li>written lesson</li>
<li>for project-based lessons, step-by-step guides on how to build the project</li>
<li>knowledge checks</li>
<li>a challenge</li>
<li>supplemental reading</li>
<li>assignment</li>
<li>post-lecture quiz</li>
</ul>
<blockquote>
<p><strong>A note about languages</strong>: These lessons are primarily written in Python, but many are also available in R. To complete an R lesson, go to the <code>/solution</code> folder and look for R lessons. They include an .rmd extension that represents an <strong>R Markdown</strong> file which can be simply defined as an embedding of <code>code chunks</code> (of R or other languages) and a <code>YAML header</code> (that guides how to format outputs such as PDF) in a <code>Markdown document</code>. As such, it serves as an exemplary authoring framework for data science since it allows you to combine your code, its output, and your thoughts by allowing you to write them down in Markdown. Moreover, R Markdown documents can be rendered to output formats such as PDF, HTML, or Word.</p>
</blockquote>
<blockquote>
<p><strong>A note about quizzes</strong>: All quizzes are contained <a href="https://gray-sand-07a10f403.1.azurestaticapps.net/">in this app</a>, for 52 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the <code>quiz-app</code> folder.</p>
</blockquote>
<table>
<thead>
<tr>
<th>Lesson Number</th>
<th>Topic</th>
<th>Lesson Grouping</th>
<th>Learning Objectives</th>
<th>Linked Lesson</th>
<th>Author</th>
</tr>
</thead>
<tbody>
<tr>
<td>01</td>
<td>Introduction to machine learning</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/1-Introduction/README.md">Introduction</a></td>
<td>Learn the basic concepts behind machine learning</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/1-Introduction/1-intro-to-ML/README.md">Lesson</a></td>
<td>Muhammad</td>
</tr>
<tr>
<td>02</td>
<td>The History of machine learning</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/1-Introduction/README.md">Introduction</a></td>
<td>Learn the history underlying this field</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/1-Introduction/2-history-of-ML/README.md">Lesson</a></td>
<td>Jen and Amy</td>
</tr>
<tr>
<td>03</td>
<td>Fairness and machine learning</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/1-Introduction/README.md">Introduction</a></td>
<td>What are the important philosophical issues around fairness that students should consider when building and applying ML models?</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/1-Introduction/3-fairness/README.md">Lesson</a></td>
<td>Tomomi</td>
</tr>
<tr>
<td>04</td>
<td>Techniques for machine learning</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/1-Introduction/README.md">Introduction</a></td>
<td>What techniques do ML researchers use to build ML models?</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/1-Introduction/4-techniques-of-ML/README.md">Lesson</a></td>
<td>Chris and Jen</td>
</tr>
<tr>
<td>05</td>
<td>Introduction to regression</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/2-Regression/README.md">Regression</a></td>
<td>Get started with Python and Scikit-learn for regression models</td>
<td><ul><li><a href="/microsoft/ML-For-Beginners/blob/main/2-Regression/1-Tools/README.md">Python</a></li><li><a href="/microsoft/ML-For-Beginners/blob/main/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb">R</a></li></ul></td>
<td><ul><li>Jen</li><li>Eric Wanjau</li></ul></td>
</tr>
<tr>
<td>06</td>
<td>North American pumpkin prices 🎃</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/2-Regression/README.md">Regression</a></td>
<td>Visualize and clean data in preparation for ML</td>
<td><ul><li><a href="/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/README.md">Python</a></li><li><a href="/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/solution/R/lesson_2-R.ipynb">R</a></li></ul></td>
<td><ul><li>Jen</li><li>Eric Wanjau</li></ul></td>
</tr>
<tr>
<td>07</td>
<td>North American pumpkin prices 🎃</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/2-Regression/README.md">Regression</a></td>
<td>Build linear and polynomial regression models</td>
<td><ul><li><a href="/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/README.md">Python</a></li><li><a href="/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb">R</a></li></ul></td>
<td><ul><li>Jen and Dmitry</li><li>Eric Wanjau</li></ul></td>
</tr>
<tr>
<td>08</td>
<td>North American pumpkin prices 🎃</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/2-Regression/README.md">Regression</a></td>
<td>Build a logistic regression model</td>
<td><ul><li><a href="/microsoft/ML-For-Beginners/blob/main/2-Regression/4-Logistic/README.md">Python</a> </li><li><a href="/microsoft/ML-For-Beginners/blob/main/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb">R</a></li></ul></td>
<td><ul><li>Jen</li><li>Eric Wanjau</li></ul></td>
</tr>
<tr>
<td>09</td>
<td>A Web App 🔌</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/3-Web-App/README.md">Web App</a></td>
<td>Build a web app to use your trained model</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/3-Web-App/1-Web-App/README.md">Python</a></td>
<td>Jen</td>
</tr>
<tr>
<td>10</td>
<td>Introduction to classification</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/4-Classification/README.md">Classification</a></td>
<td>Clean, prep, and visualize your data; introduction to classification</td>
<td><ul><li> <a href="/microsoft/ML-For-Beginners/blob/main/4-Classification/1-Introduction/README.md">Python</a> </li><li><a href="/microsoft/ML-For-Beginners/blob/main/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb">R</a></li></ul></td>
<td><ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul></td>
</tr>
<tr>
<td>11</td>
<td>Delicious Asian and Indian cuisines 🍜</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/4-Classification/README.md">Classification</a></td>
<td>Introduction to classifiers</td>
<td><ul><li> <a href="/microsoft/ML-For-Beginners/blob/main/4-Classification/2-Classifiers-1/README.md">Python</a></li><li><a href="/microsoft/ML-For-Beginners/blob/main/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb">R</a></li></ul></td>
<td><ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul></td>
</tr>
<tr>
<td>12</td>
<td>Delicious Asian and Indian cuisines 🍜</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/4-Classification/README.md">Classification</a></td>
<td>More classifiers</td>
<td><ul><li> <a href="/microsoft/ML-For-Beginners/blob/main/4-Classification/3-Classifiers-2/README.md">Python</a></li><li><a href="/microsoft/ML-For-Beginners/blob/main/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb">R</a></li></ul></td>
<td><ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul></td>
</tr>
<tr>
<td>13</td>
<td>Delicious Asian and Indian cuisines 🍜</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/4-Classification/README.md">Classification</a></td>
<td>Build a recommender web app using your model</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/4-Classification/4-Applied/README.md">Python</a></td>
<td>Jen</td>
</tr>
<tr>
<td>14</td>
<td>Introduction to clustering</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/5-Clustering/README.md">Clustering</a></td>
<td>Clean, prep, and visualize your data; Introduction to clustering</td>
<td><ul><li> <a href="/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/README.md">Python</a></li><li><a href="/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb">R</a></li></ul></td>
<td><ul><li>Jen</li><li>Eric Wanjau</li></ul></td>
</tr>
<tr>
<td>15</td>
<td>Exploring Nigerian Musical Tastes 🎧</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/5-Clustering/README.md">Clustering</a></td>
<td>Explore the K-Means clustering method</td>
<td><ul><li> <a href="/microsoft/ML-For-Beginners/blob/main/5-Clustering/2-K-Means/README.md">Python</a></li><li><a href="/microsoft/ML-For-Beginners/blob/main/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb">R</a></li></ul></td>
<td><ul><li>Jen</li><li>Eric Wanjau</li></ul></td>
</tr>
<tr>
<td>16</td>
<td>Introduction to natural language processing ☕️</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/6-NLP/README.md">Natural language processing</a></td>
<td>Learn the basics about NLP by building a simple bot</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/6-NLP/1-Introduction-to-NLP/README.md">Python</a></td>
<td>Stephen</td>
</tr>
<tr>
<td>17</td>
<td>Common NLP Tasks ☕️</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/6-NLP/README.md">Natural language processing</a></td>
<td>Deepen your NLP knowledge by understanding common tasks required when dealing with language structures</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/6-NLP/2-Tasks/README.md">Python</a></td>
<td>Stephen</td>
</tr>
<tr>
<td>18</td>
<td>Translation and sentiment analysis ♥️</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/6-NLP/README.md">Natural language processing</a></td>
<td>Translation and sentiment analysis with Jane Austen</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/6-NLP/3-Translation-Sentiment/README.md">Python</a></td>
<td>Stephen</td>
</tr>
<tr>
<td>19</td>
<td>Romantic hotels of Europe ♥️</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/6-NLP/README.md">Natural language processing</a></td>
<td>Sentiment analysis with hotel reviews 1</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/6-NLP/4-Hotel-Reviews-1/README.md">Python</a></td>
<td>Stephen</td>
</tr>
<tr>
<td>20</td>
<td>Romantic hotels of Europe ♥️</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/6-NLP/README.md">Natural language processing</a></td>
<td>Sentiment analysis with hotel reviews 2</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/6-NLP/5-Hotel-Reviews-2/README.md">Python</a></td>
<td>Stephen</td>
</tr>
<tr>
<td>21</td>
<td>Introduction to time series forecasting</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/7-TimeSeries/README.md">Time series</a></td>
<td>Introduction to time series forecasting</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/7-TimeSeries/1-Introduction/README.md">Python</a></td>
<td>Francesca</td>
</tr>
<tr>
<td>22</td>
<td>⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/7-TimeSeries/README.md">Time series</a></td>
<td>Time series forecasting with ARIMA</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/7-TimeSeries/2-ARIMA/README.md">Python</a></td>
<td>Francesca</td>
</tr>
<tr>
<td>23</td>
<td>⚡️ World Power Usage ⚡️ - time series forecasting with SVR</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/7-TimeSeries/README.md">Time series</a></td>
<td>Time series forecasting with Support Vector Regressor</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/7-TimeSeries/3-SVR/README.md">Python</a></td>
<td>Anirban</td>
</tr>
<tr>
<td>24</td>
<td>Introduction to reinforcement learning</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/8-Reinforcement/README.md">Reinforcement learning</a></td>
<td>Introduction to reinforcement learning with Q-Learning</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/8-Reinforcement/1-QLearning/README.md">Python</a></td>
<td>Dmitry</td>
</tr>
<tr>
<td>25</td>
<td>Help Peter avoid the wolf! 🐺</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/8-Reinforcement/README.md">Reinforcement learning</a></td>
<td>Reinforcement learning Gym</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/8-Reinforcement/2-Gym/README.md">Python</a></td>
<td>Dmitry</td>
</tr>
<tr>
<td>Postscript</td>
<td>Real-World ML scenarios and applications</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/9-Real-World/README.md">ML in the Wild</a></td>
<td>Interesting and revealing real-world applications of classical ML</td>
<td><a href="/microsoft/ML-For-Beginners/blob/main/9-Real-World/1-Applications/README.md">Lesson</a></td>
<td>Team</td>
</tr>
</tbody>
</table>
<h2>Offline access</h2>
<p>You can run this documentation offline by using <a href="https://docsify.js.org/#/">Docsify</a>. Fork this repo, <a href="https://docsify.js.org/#/quickstart">install Docsify</a> on your local machine, and then in the root folder of this repo, type <code>docsify serve</code>. The website will be served on port 3000 on your localhost: <code>localhost:3000</code>.</p>
<h2>PDFs</h2>
<p>Find a pdf of the curriculum with links <a href="https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf">here</a>.</p>
<h2>Help Wanted!</h2>
<p>Would you like to contribute a translation? Please read our <a href="/microsoft/ML-For-Beginners/blob/main/TRANSLATIONS.md">translation guidelines</a> and add a templated issue to manage the workload <a href="https://github.com/microsoft/ML-For-Beginners/issues">here</a>.</p>
<h2>Other Curricula</h2>
<p>Our team produces other curricula! Check out:</p>
<ul>
<li><a href="https://aka.ms/webdev-beginners">Web Dev for Beginners</a></li>
<li><a href="https://aka.ms/iot-beginners">IoT for Beginners</a></li>
<li><a href="https://aka.ms/datascience-beginners">Data Science for Beginners</a></li>
<li><a href="https://aka.ms/ai-beginners">AI for Beginners</a></li>
</ul>
</article>
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 Machine Learning for Beginners - A Curriculum Getting Started Video walkthroughs Meet the Team Pedagogy Each lesson includes: Offline access PDFs Help Wanted! Other Curricula

 README.md

 Machine Learning for Beginners - A Curriculum

 🌍 Travel around the world as we explore Machine Learning by means of world cultures 🌍

 Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about Machine Learning. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. Pair these lessons with our 'Data Science for Beginners' curriculum, as well!

 Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment, and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.

 ✍️ Hearty thanks to our authors Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, and Amy Boyd

 🎨 Thanks as well to our illustrators Tomomi Imura, Dasani Madipalli, and Jen Looper

 🙏 Special thanks 🙏 to our Microsoft Student Ambassador authors, reviewers, and content contributors, notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal

 🤩 Extra gratitude to Microsoft Student Ambassador Eric Wanjau for our R lessons!

 Getting Started

 Students, to use this curriculum, fork the entire repo to your own GitHub account and complete the exercises on your own or with a group:

 * Start with a pre-lecture quiz.
 * Read the lecture and complete the activities, pausing and reflecting at each knowledge check.
 * Try to create the projects by comprehending the lessons rather than running the solution code; however that code is available in the /solution folders in each project-oriented lesson.
 * Take the post-lecture quiz.
 * Complete the challenge.
 * Complete the assignment.
 * After completing a lesson group, visit the Discussion Board and "learn out loud" by filling out the appropriate PAT rubric. A 'PAT' is a Progress Assessment Tool that is a rubric you fill out to further your learning. You can also react to other PATs so we can learn together.

 For further study, we recommend following these Microsoft Learn modules and learning paths.

 Teachers, we have included some suggestions on how to use this curriculum.

 Video walkthroughs

 Some of the lessons are available as short form video. You can find all these in-line in the lessons, or on the ML for Beginners playlist on the Microsoft Developer YouTube channel by clicking the image below.

 Meet the Team

 Gif by Mohit Jaisal

 🎥 Click the image above for a video about the project and the folks who created it!

 Pedagogy

 We have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on project-based and that it includes frequent quizzes. In addition, this curriculum has a common theme to give it cohesion.

 By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12-week cycle. This curriculum also includes a postscript on real-world applications of ML, which can be used as extra credit or as a basis for discussion.

 Find our Code of Conduct, Contributing, and Translation guidelines. We welcome your constructive feedback!

 Each lesson includes:

 * optional sketchnote
 * optional supplemental video
 * video walkthrough (some lessons only)
 * pre-lecture warmup quiz
 * written lesson
 * for project-based lessons, step-by-step guides on how to build the project
 * knowledge checks
 * a challenge
 * supplemental reading
 * assignment
 * post-lecture quiz

 A note about languages: These lessons are primarily written in Python, but many are also available in R. To complete an R lesson, go to the /solution folder and look for R lessons. They include an .rmd extension that represents an R Markdown file which can be simply defined as an embedding of code chunks (of R or other languages) and a YAML header (that guides how to format outputs such as PDF) in a Markdown document. As such, it serves as an exemplary authoring framework for data science since it allows you to combine your code, its output, and your thoughts by allowing you to write them down in Markdown. Moreover, R Markdown documents can be rendered to output formats such as PDF, HTML, or Word.

 A note about quizzes: All quizzes are contained in this app, for 52 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the quiz-app folder.

 Lesson Number Topic Lesson Grouping Learning Objectives  Linked Lesson Author 
 01 Introduction to machine learning Introduction Learn the basic concepts behind machine learning  Lesson Muhammad 
 02 The History of machine learning Introduction Learn the history underlying this field  Lesson Jen and Amy 
 03 Fairness and machine learning Introduction What are the important philosophical issues around fairness that students should consider when building and applying ML models? Lesson Tomomi 
 04 Techniques for machine learning Introduction What techniques do ML researchers use to build ML models?  Lesson Chris and Jen 
 05 Introduction to regression Regression Get started with Python and Scikit-learn for regression models  * Python * Jen 
     * R * Eric Wanjau 
 06 North American pumpkin prices 🎃 Regression Visualize and clean data in preparation for ML  * Python * Jen 
     * R * Eric Wanjau 
 07 North American pumpkin prices 🎃 Regression Build linear and polynomial regression models  * Python * Jen and Dmitry
     * R * Eric Wanjau 
 08 North American pumpkin prices 🎃 Regression Build a logistic regression model  * Python * Jen 
     * R * Eric Wanjau 
 09 A Web App 🔌 Web App Build a web app to use your trained model  Python Jen 
 10 Introduction to classification Classification Clean, prep, and visualize your data; introduction to classification * Python * Jen and Cassie
     * R * Eric Wanjau 
 11 Delicious Asian and Indian cuisines 🍜 Classification Introduction to classifiers  * Python * Jen and Cassie
     * R * Eric Wanjau 
 12 Delicious Asian and Indian cuisines 🍜 Classification More classifiers  * Python * Jen and Cassie
     * R * Eric Wanjau 
 13 Delicious Asian and Indian cuisines 🍜 Classification Build a recommender web app using your model  Python Jen 
 14 Introduction to clustering Clustering Clean, prep, and visualize your data; Introduction to clustering  * Python * Jen 
     * R * Eric Wanjau 
 15 Exploring Nigerian Musical Tastes 🎧 Clustering Explore the K-Means clustering method  * Python * Jen 
     * R * Eric Wanjau 
 16 Introduction to natural language processing ☕️ Natural language processing Learn the basics about NLP by building a simple bot  Python Stephen 
 17 Common NLP Tasks ☕️ Natural language processing Deepen your NLP knowledge by understanding common tasks required when dealing with language structures Python Stephen 
 18 Translation and sentiment analysis ♥️ Natural language processing Translation and sentiment analysis with Jane Austen  Python Stephen 
 19 Romantic hotels of Europe ♥️ Natural language processing Sentiment analysis with hotel reviews 1  Python Stephen 
 20 Romantic hotels of Europe ♥️ Natural language processing Sentiment analysis with hotel reviews 2  Python Stephen 
 21 Introduction to time series forecasting Time series Introduction to time series forecasting  Python Francesca 
 22 ⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA Time series Time series forecasting with ARIMA  Python Francesca 
 23 ⚡️ World Power Usage ⚡️ - time series forecasting with SVR Time series Time series forecasting with Support Vector Regressor  Python Anirban 
 24 Introduction to reinforcement learning Reinforcement learning Introduction to reinforcement learning with Q-Learning  Python Dmitry 
 25 Help Peter avoid the wolf! 🐺 Reinforcement learning Reinforcement learning Gym  Python Dmitry 
 Postscript Real-World ML scenarios and applications ML in the Wild Interesting and revealing real-world applications of classical ML  Lesson Team 
 

 Offline access

 You can run this documentation offline by using Docsify. Fork this repo, install Docsify on your local machine, and then in the root folder of this repo, type docsify serve. The website will be served on port 3000 on your localhost: localhost:3000.

 PDFs

 Find a pdf of the curriculum with links here.

 Help Wanted!

 Would you like to contribute a translation? Please read our translation guidelines and add a templated issue to manage the workload here.

 Other Curricula

 Our team produces other curricula! Check out:

 * Web Dev for Beginners
 * IoT for Beginners
 * Data Science for Beginners
 * AI for Beginners

 About

 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

 microsoft.github.io/ML-For-Beginners/

 Topics

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 License

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Machine Learning for Beginners - A Curriculum
🌍 Travel around the world as we explore Machine Learning by means of world cultures 🌍
Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about Machine Learning. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. Pair these lessons with our 'Data Science for Beginners' curriculum, as well!
Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment, and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.
 ✍️ Hearty thanks to our authors Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, and Amy Boyd
 🎨 Thanks as well to our illustrators Tomomi Imura, Dasani Madipalli, and Jen Looper
 🙏 Special thanks 🙏 to our Microsoft Student Ambassador authors, reviewers, and content contributors, notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
 🤩 Extra gratitude to Microsoft Student Ambassador Eric Wanjau for our R lessons! 
Getting Started
  Students , to use this curriculum, fork the entire repo to your own GitHub account and complete the exercises on your own or with a group:
Start with a pre-lecture quiz.
Read the lecture and complete the activities, pausing and reflecting at each knowledge check.
Try to create the projects by comprehending the lessons rather than running the solution code; however that code is available in the /solution folders in each project-oriented lesson.
Take the post-lecture quiz.
Complete the challenge.
Complete the assignment.
After completing a lesson group, visit the Discussion Board and "learn out loud" by filling out the appropriate PAT rubric. A 'PAT' is a Progress Assessment Tool that is a rubric you fill out to further your learning. You can also react to other PATs so we can learn together.
For further study, we recommend following these Microsoft Learn modules and learning paths.
 Teachers, we have included some suggestions on how to use this curriculum.
Video walkthroughs
Some of the lessons are available as short form video. You can find all these in-line in the lessons, or on the ML for Beginners playlist on the Microsoft Developer YouTube channel by clicking the image below.
   
Meet the Team
   
 Gif by Mohit Jaisal 
🎥 Click the image above for a video about the project and the folks who created it!
Pedagogy
We have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on project-based and that it includes frequent quizzes. In addition, this curriculum has a common theme to give it cohesion.
By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12-week cycle. This curriculum also includes a postscript on real-world applications of ML, which can be used as extra credit or as a basis for discussion.
Find our Code of Conduct, Contributing, and Translation guidelines. We welcome your constructive feedback!
Each lesson includes:
optional sketchnote
optional supplemental video
video walkthrough (some lessons only)
pre-lecture warmup quiz
written lesson
for project-based lessons, step-by-step guides on how to build the project
knowledge checks
a challenge
supplemental reading
assignment
post-lecture quiz
 A note about languages: These lessons are primarily written in Python, but many are also available in R. To complete an R lesson, go to the /solution folder and look for R lessons. They include an .rmd extension that represents an R Markdown file which can be simply defined as an embedding of code chunks (of R or other languages) and a YAML header (that guides how to format outputs such as PDF) in a Markdown document. As such, it serves as an exemplary authoring framework for data science since it allows you to combine your code, its output, and your thoughts by allowing you to write them down in Markdown. Moreover, R Markdown documents can be rendered to output formats such as PDF, HTML, or Word.
 A note about quizzes: All quizzes are contained in this app, for 52 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the quiz-app folder.
Lesson Number
Topic
Lesson Grouping
Learning Objectives
Linked Lesson
Author
01
Introduction to machine learning
 Introduction 
Learn the basic concepts behind machine learning
 Lesson 
Muhammad
02
The History of machine learning
 Introduction 
Learn the history underlying this field
 Lesson 
Jen and Amy
03
Fairness and machine learning
 Introduction 
What are the important philosophical issues around fairness that students should consider when building and applying ML models?
 Lesson 
Tomomi
04
Techniques for machine learning
 Introduction 
What techniques do ML researchers use to build ML models?
 Lesson 
Chris and Jen
05
Introduction to regression
 Regression 
Get started with Python and Scikit-learn for regression models
   Python   R   
  Jen Eric Wanjau  
06
North American pumpkin prices 🎃
 Regression 
Visualize and clean data in preparation for ML
   Python   R   
  Jen Eric Wanjau  
07
North American pumpkin prices 🎃
 Regression 
Build linear and polynomial regression models
   Python   R   
  Jen and Dmitry Eric Wanjau  
08
North American pumpkin prices 🎃
 Regression 
Build a logistic regression model
   Python   R   
  Jen Eric Wanjau  
09
A Web App 🔌
 Web App 
Build a web app to use your trained model
 Python 
Jen
10
Introduction to classification
 Classification 
Clean, prep, and visualize your data; introduction to classification
   Python   R   
  Jen and Cassie Eric Wanjau  
11
Delicious Asian and Indian cuisines 🍜
 Classification 
Introduction to classifiers
   Python   R   
  Jen and Cassie Eric Wanjau  
12
Delicious Asian and Indian cuisines 🍜
 Classification 
More classifiers
   Python   R   
  Jen and Cassie Eric Wanjau  
13
Delicious Asian and Indian cuisines 🍜
 Classification 
Build a recommender web app using your model
 Python 
Jen
14
Introduction to clustering
 Clustering 
Clean, prep, and visualize your data; Introduction to clustering
   Python   R   
  Jen Eric Wanjau  
15
Exploring Nigerian Musical Tastes 🎧
 Clustering 
Explore the K-Means clustering method
   Python   R   
  Jen Eric Wanjau  
16
Introduction to natural language processing ☕️
 Natural language processing 
Learn the basics about NLP by building a simple bot
 Python 
Stephen
17
Common NLP Tasks ☕️
 Natural language processing 
Deepen your NLP knowledge by understanding common tasks required when dealing with language structures
 Python 
Stephen
18
Translation and sentiment analysis ♥️
 Natural language processing 
Translation and sentiment analysis with Jane Austen
 Python 
Stephen
19
Romantic hotels of Europe ♥️
 Natural language processing 
Sentiment analysis with hotel reviews 1
 Python 
Stephen
20
Romantic hotels of Europe ♥️
 Natural language processing 
Sentiment analysis with hotel reviews 2
 Python 
Stephen
21
Introduction to time series forecasting
 Time series 
Introduction to time series forecasting
 Python 
Francesca
22
⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA
 Time series 
Time series forecasting with ARIMA
 Python 
Francesca
23
⚡️ World Power Usage ⚡️ - time series forecasting with SVR
 Time series 
Time series forecasting with Support Vector Regressor
 Python 
Anirban
24
Introduction to reinforcement learning
 Reinforcement learning 
Introduction to reinforcement learning with Q-Learning
 Python 
Dmitry
25
Help Peter avoid the wolf! 🐺
 Reinforcement learning 
Reinforcement learning Gym
 Python 
Dmitry
Postscript
Real-World ML scenarios and applications
 ML in the Wild 
Interesting and revealing real-world applications of classical ML
 Lesson 
Team
Offline access
You can run this documentation offline by using Docsify. Fork this repo, install Docsify on your local machine, and then in the root folder of this repo, type docsify serve. The website will be served on port 3000 on your localhost: localhost:3000.
PDFs
Find a pdf of the curriculum with links here.
Help Wanted!
Would you like to contribute a translation? Please read our translation guidelines and add a templated issue to manage the workload here.
Other Curricula
Our team produces other curricula! Check out:
 Web Dev for Beginners 
 IoT for Beginners 
 Data Science for Beginners 
 AI for Beginners
GitHub - microsoft/ML-For-Beginners: 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
https://github.com/microsoft/ML-For-Beginners
http://neuralnetworksanddeeplearning.com/
Neural Networks and Deep Learning
2015-01-01
Nielsen; Michael A
<div><div>
<p>
<em>Neural Networks and Deep Learning</em> is a free online book. The
book will teach you about:
</p><ul>
<li>Neural networks, a beautiful biologically-inspired programming
paradigm which enables a computer to learn from observational data
</li><li>Deep learning, a powerful set of techniques for learning in neural
networks
</li></ul><p>
Neural networks and deep learning currently provide the best solutions
to many problems in image recognition, speech recognition, and natural
language processing. This book will teach you many of the core
concepts behind neural networks and deep learning. 
</p>
<p>For more details about the approach taken in the
book, <a href="about.html">see here</a>. Or you can jump directly
to <a href="chap1.html">Chapter 1</a> and get started.
</p>
</div></div>||||I|||| Neural Networks and Deep Learning

 Neural Networks and Deep Learning

 What this book is about

 On the exercises and problems

 Using neural nets to recognize handwritten digits

 How the backpropagation algorithm works

 Improving the way neural networks learn

 A visual proof that neural nets can compute any function

 Why are deep neural networks hard to train?

 Deep learning

 Appendix: Is there a simple algorithm for intelligence?

 Acknowledgements

 Frequently Asked Questions

 If you benefit from the book, please make a small donation. I suggest $5, but you can choose the amount.

 Alternately, you can make a donation by sending me Bitcoin, at address 1Kd6tXH5SDAmiFb49J9hknG5pqj7KStSAx

 Sponsors

 Deep Learning Workstations, Servers, and Laptops


 Thanks to all the supporters who made the book possible, with especial thanks to Pavel Dudrenov. Thanks also to all the contributors to the Bugfinder Hall of Fame.

 Resources

 Michael Nielsen on Twitter

 Book FAQ

 Code repository

 Michael Nielsen's project announcement mailing list

 Deep Learning, book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

 cognitivemedium.com

 By Michael Nielsen / Dec 2019

 Neural Networks and Deep Learning is a free online book. The book will teach you about:

 * Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
 * Deep learning, a powerful set of techniques for learning in neural networks
 Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.

 For more details about the approach taken in the book, see here. Or you can jump directly to Chapter 1 and get started.





































 In academic work, please cite this book as: Michael A. Nielsen, "Neural Networks and Deep Learning", Determination Press, 2015

 This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License . This means you're free to copy, share, and build on this book, but not to sell it. If you're interested in commercial use, please contact me . Last update: Thu Dec 26 15:26:33 2019


Neural Networks and Deep Learning is a free online book. The
book will teach you about:
 
Neural networks, a beautiful biologically-inspired programming
paradigm which enables a computer to learn from observational data
 Deep learning, a powerful set of techniques for learning in neural
networks
  
Neural networks and deep learning currently provide the best solutions
to many problems in image recognition, speech recognition, and natural
language processing. This book will teach you many of the core
concepts behind neural networks and deep learning. 
For more details about the approach taken in the
book, see here. Or you can jump directly
to Chapter 1 and get started.
Neural Networks and Deep Learning
http://neuralnetworksanddeeplearning.com/