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Best Machine Learning Books

Machine Learning is one of the hottest domains of Computer Science. We scoured the web for every book on machine learning, compiled a list and ranked them by how often they were featured.

Recommendations from 30 articles, Bill Gates, Mark Cuban, Tim O’Reilly and 16 others.
76 books on the list
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Deep Learning book cover
Deep Learning
Ian Goodfellow - Nov 17, 2016
Goodreads Rating
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the comp...
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow book cover
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Concepts, Tools, and Techniques to Build Intelligent Systems
Aurélien Géron - Oct 14, 2019
Goodreads Rating
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two pro...
Deep Learning with Python book cover
Deep Learning with Python
François Chollet - Dec 21, 2017
Goodreads Rating
Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on ...
Pattern Recognition and Machine Learning book cover
Pattern Recognition and Machine Learning
Christopher M. Bishop - Aug 16, 2006
Goodreads Rating
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream,...
The Hundred-Page Machine Learning Book book cover
The Hundred-Page Machine Learning Book
Andriy Burkov - Jan 13, 2019
Goodreads Rating
WARNING! To avoid buying counterfeit on Amazon, click on "See All Buying Options" and choose "" and not a third-party seller.Concise and to the point — the book can be read during a week. During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning th...
Recommended by
Kirk Borne
Machine Learning book cover
Machine Learning
A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Kevin P. Murphy - Aug 23, 2012
Goodreads Rating
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to pre...
Recommended by
Kirk Borne
Deep Learning book cover
Deep Learning
A Practitioner's Approach
Josh Patterson - Aug 22, 2017
Goodreads Rating
Looking for one central source where you can learn key findings on machine learning? Deep Learning: The Definitive Guide provides developers and data scientists with the most practical information available on the subject, including deep learning theory, best practices, and use cases.Authors Adam Gibson and Josh Patterson present the latest relevan...
Introduction to Machine Learning with Python book cover
Introduction to Machine Learning with Python
Andreas Muller - Nov 10, 2016 (first published in 2015)
Goodreads Rating
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine ...
The Elements of Statistical Learning book cover
The Elements of Statistical Learning
Data Mining, Inference, and Prediction (Springer Series in Statistics)
Trevor Hastie - Jul 29, 2003
Goodreads Rating
During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data...
Machine Learning with TensorFlow book cover
Machine Learning with TensorFlow
Nishant Shukla - Feb 12, 2018
Goodreads Rating
SummaryMachine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyTensorFlow, Google's library for large-scale machine learning, s...
Python Machine Learning book cover
Python Machine Learning
Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition
Sebastian Raschka - Dec 12, 2019
Goodreads Rating
Link to the GitHub Repository containing the code examples and additional material: of the most innovative breakthroughs and exciting new technologies can be attributed to applications of machine learning. We are living in an age where data comes in abundance, and thanks to the self-learning algorithms f...
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Kirk BorneCraig Brown
Deep Reinforcement Learning Hands-On by Maxim Lapan
An Introduction to Statistical Learning by Gareth James
Grokking Deep Learning by Andrew Trask
Machine Learning For Absolute Beginners by Oliver Theobald
Neural Networks for Pattern Recognition by Christopher M. Bishop
Programming Collective Intelligence by Segaran
Machine Learning by Peter Flach
Neural Smithing by Russell Reed
Data Mining by Ian H. Witten
TensorFlow Machine Learning Cookbook by Nick McClure
Fundamentals of Deep Learning by Nikhil Buduma
Machine Learning for Hackers by Drew Conway
Machine Learning with R by Brett Lantz
The Book of Why by Judea Pearl
Make Your Own Neural Network by Tariq Rashid
Advances in Financial Machine Learning by Marcos Lopez de Prado
Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher
Understanding Machine Learning by Shai Shalev-Shwartz
Machine Learning by Tom M. Mitchell
Reinforcement Learning by Richard S. Sutton
Data Science from Scratch by Joel Grus
Machine Learning in Action by Peter Harrington
Machine Learning For Dummies by John Paul Mueller
Data Science for Business by Foster Provost
Applied Predictive Modeling by Max Kuhn
Generative Deep Learning by David Foster
R for Data Science by Hadley Wickham
Python Data Science Handbook by Jake Vanderplas
Data Smart by John W. Foreman
Bayesian Data Analysis by Andrew Gelman
Forecasting by Rob J Hyndman
Probabilistic Graphical Models by Daphne Koller
Neural Network Design by Martin T Hagan
Deep Learning by Michael Fullan
Deeper Learning by Monica Martinez
Neural Networks and Deep Learning by Charu C. Aggarwal
Machine Learning by Sergios Theodoridis
Natural Language Processing in Action by Hobson Lane
Practical Data Science with R by Nina Zumel
Deep Learning and the Game of Go by Max Pumperla
Real-World Machine Learning by Henrik Brink
Natural Language Processing with Python by Steven Bird
Hands-On Deep Learning Algorithms with Python by Sudharsan Ravichandiran
Mastering .NET Machine Learning by Jamie Dixon
Fundamentals of Artificial Neural Networks by Mohamad Hassoun
Foundations of Machine Learning by Mehryar Mohri
Mastering Machine Learning with R by Cory Lesmeister
Bayesian Reasoning and Machine Learning by David Barber
Advanced Deep Learning with Keras by Rowel Atienza
Deep Learning Illustrated by Jon Krohn
Building Machine Learning Powered Applications by Emmanuel Ameisen
ApproachingAny Machine Learning Problem by Abhishek Thakur
TinyML by Pete Warden
Practical Time Series Forecasting with R by Galit Shmueli
Artificial Intelligence for Humans, Volume 2 by Jeff Heaton
The Signal and the Noise by Nate Silver
Pattern Classification 2nd Edition with Computer Manual 2nd Edition Set by Richard O. Duda
Rebooting AI by Gary Marcus
Weapons of Math Destruction by Cathy O'Neil
The Drunkard's Walk by Leonard Mlodinow
Naked Statistics by Charles Wheelan
The Master Algorithm by Pedro Domingos
Artificial Intelligence for Humans, Volume 3 by Jeff Heaton
Neural Networks by Simon Haykin
Machine Learning by Stephen Marsland