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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 51 articles, Barack Obama, Bill Gates, Elon Musk and 37 others.
Best Machine Learning Books
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Deep Learning book cover
Deep Learning
Ian Goodfellow - 2016-11-17
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 - 2019-10-14
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...
The Hundred-Page Machine Learning Book book cover
The Hundred-Page Machine Learning Book
Andriy Burkov - 2019-01-13
Goodreads Rating
WARNING! To avoid buying counterfeit on Amazon, click on "See All Buying Options" and choose "Amazon.com" 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
Pattern Recognition and Machine Learning book cover
Pattern Recognition and Machine Learning
Christopher M. Bishop - 2006-08-16
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,...
Introduction to Machine Learning with Python book cover
Introduction to Machine Learning with Python
Andreas Muller - 2016-11-10 (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 - 2003-07-29
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 For Absolute Beginners book cover
Machine Learning For Absolute Beginners
A Plain English Introduction (Machine Learning From Scratch)
Oliver Theobald - 2018-01-01
Goodreads Rating
Featured by Tableau as the first of "7 Books About Machine Learning for Beginners" Ready to crank up a virtual server and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?Well, hold on there...Before you embark on your epic journey into the world of machine learning, there is some theory and statistical princ...
Deep Learning with Python book cover
Deep Learning with Python
François Chollet - 2017-12-21
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 ...
Machine Learning book cover
Machine Learning
A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Kevin P. Murphy - 2012-08-23
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
Python Machine Learning book cover
Python Machine Learning
Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition
Sebastian Raschka - 2019-12-12
Goodreads Rating
Link to the GitHub Repository containing the code examples and additional material: https://github.com/rasbt/python-machi...Many 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
Machine Learning for Hackers by Drew Conway
An Introduction to Statistical Learning by Gareth James
Machine Learning by Tom M. Mitchell
Data Mining by Ian H. Witten
Deep Learning by Josh Patterson
Machine Learning For Dummies by John Paul Mueller
Machine Learning with TensorFlow by Nishant Shukla
Applied Predictive Modeling by Max Kuhn
Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher
Machine Learning by Peter Flach
Fundamentals of Deep Learning by Nikhil Buduma
Machine Learning in Action by Peter Harrington
Machine Learning with R by Brett Lantz
Deep Reinforcement Learning Hands-On by Maxim Lapan
Artificial Intelligence by Stuart Russell
Advances in Financial Machine Learning by Marcos Lopez de Prado
Understanding Machine Learning by Shai Shalev-Shwartz
Natural Language Processing with Python by Steven Bird
Grokking Deep Learning by Andrew Trask
Neural Networks for Pattern Recognition by Christopher M. Bishop
Make Your Own Neural Network by Tariq Rashid
Learning from Data by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin
Neural Smithing by Russell Reed
Machine Learning by Stephen Marsland
TensorFlow Machine Learning Cookbook by Nick McClure
Deep Learning for Coders with fastai and PyTorch by Jeremy Howard
Generative Deep Learning by David Foster
The Book of Why by Judea Pearl
Probabilistic Graphical Models by Daphne Koller
Machine Learning by Sergios Theodoridis
Practical Data Science with R by Nina Zumel
Bayesian Reasoning and Machine Learning by David Barber
Reinforcement Learning by Richard S. Sutton
Data Science from Scratch by Joel Grus
Speech and Language Processing by Daniel Jurafsky
AI and Machine Learning for Coders by Laurence Moroney
Machine Learning and Data Science Blueprints for Finance by Hariom Tatsat, Sahil Puri, Brad Lookabaugh
The Master Algorithm by Pedro Domingos
Prediction Machines by Ajay Agrawal
Artificial Intelligence for Humans by Jeff Heaton
Python Machine Learning By Example by Yuxi (Hayden) Liu
Machine Learning by Ethem Alpaydin
AI Superpowers by Kai-fu Lee
Life 3.0 by Max Tegmark
The Singularity Is Near by Ray Kurzweil
Data Science for Business by Foster Provost
Data Smart by John W. Foreman
Bayesian Data Analysis by Andrew Gelman
Forecasting by Rob J Hyndman
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
Natural Language Processing in Action by Hobson Lane
Real-World Machine Learning by Henrik Brink
Hands-On Deep Learning Algorithms with Python by Sudharsan Ravichandiran
Foundations of Machine Learning by Mehryar Mohri
Paradigms of Artificial Intelligence Programming by Peter Norvig
Deep Learning Illustrated by Jon Krohn
Machine Learning for Algorithmic Trading by Stefan Jansen
Machine Learning Design Patterns by Valliappa Lakshmanan
Building Machine Learning Powered Applications by Emmanuel Ameisen
ApproachingAny Machine Learning Problem by Abhishek Thakur
TinyML by Pete Warden
R for Data Science by Hadley Wickham
Python Data Science Handbook by Jake Vanderplas
Machine Learning Engineering by Andriy Burkov
Forecasting by Rob Hyndman, George Athanasopoulos
Computer Programming And Cyber Security for Beginners by Zach Codings
Machine Learning with Python by Oliver Theobald
Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili
Machine Learning with Python Cookbook by Chris Albon
Artificial Intelligence and Machine Learning for Business by Steven Finlay
The Selling Revolution by DJ Sebastian
Machine Learning by Ethem Mining
Ultimate Step by Step Guide to Machine Learning Using Python by Daneyal Anis
Machine Learning in Finance by Matthew F. Dixon, Igor Halperin, Paul Bilokon
Machine Learning Pocket Reference by Matt Harrison
Practical Natural Language Processing by Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, Harshit Surana
Deep Learning in Production by Sergios Karagiannakos
Grokking Machine Learning by Luis Serrano
Undefined (The Elemental Saga) by Jessica Ruddick
Algorithmic Trading by Jeffrey M Bacidore