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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 38 others.
Best Machine Learning Books
94 books on the list
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Deep Learning book cover
Deep Learning
Ian Goodfellow - 2016-11-01
Goodreads Rating
This book explores the fascinating world of deep learning, which teaches computers to understand the world through a hierarchy of concepts. It covers mathematical and conceptual background, techniques used in industry, and research perspectives. Readers will learn about relevant topics in linear algebra, probability theory, and more, as well as practical applications in areas like natural language processing and computer vision. Perfect for students or engineers looking to incorporate deep learning into their work. Supplementary material available on the website.
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 - 2017-04-09
Goodreads Rating
This practical book is perfect for programmers interested in delving into the exciting field of machine learning. With concrete examples, minimal theory, and Python frameworks like Scikit-Learn and TensorFlow, the author shows readers how to build intelligent systems capable of learning from data. From simple linear regression to deep neural networks, you'll gain an intuitive understanding of machine learning techniques and architectures while getting hands-on experience through exercises in each chapter. Dive into the machine learning landscape and start building intelligent systems today.
The Hundred-Page Machine Learning Book book cover
The Hundred-Page Machine Learning Book
Andriy Burkov - 2019-01-13
Goodreads Rating
This machine learning book covers everything modern machine learning has to offer and can be read in just one week. It's concise and up to date, written by an experienced practitioner. Plus, it has a continuously updated wiki with additional resources. With flexible pricing and formats, you can choose what suits you best, and you can even read the book chapters for free before deciding whether to buy.
Recommended by
Kirk Borne
Pattern Recognition and Machine Learning book cover
Pattern Recognition and Machine Learning
Christopher M. Bishop - 2006-08-17
Goodreads Rating
"Discover the cutting-edge world of pattern recognition and machine learning with this comprehensive textbook. Bayesian methods and graphical models have transformed these fields in the past decade, and this book explores them while providing a thorough introduction to the subject matter. Perfect for advanced students, researchers, and practitioners, this book assumes no prior knowledge of the concepts and includes a self-contained introduction to basic probability theory. Get ready to dive into the exciting world of pattern recognition and machine learning!"
Introduction to Machine Learning with Python book cover
Introduction to Machine Learning with Python
Andreas Muller - 2016-10-20 (first published in 2015)
Goodreads Rating
Learn practical ways to build your own machine learning solutions using Python with this book! Perfect for beginners, authors Andreas Müller and Sarah Guido will guide you through the process of creating successful machine-learning applications with the scikit-learn library. You'll discover how to use machine learning algorithms and focus on the practical aspects of the process. Don't wait to unlock the full potential of machine learning with Python!
The Elements of Statistical Learning book cover
The Elements of Statistical Learning
Data Mining, Inference, and Prediction (Springer Series in Statistics)
Trevor Hastie - 2003-07-30 (first published in 2001)
Goodreads Rating
Discover the world of data mining and machine learning with this comprehensive guide. Written by three prominent professors of statistics, this book provides a common conceptual framework for understanding the tools and ideas in various fields such as medicine, biology, finance, and marketing. With a focus on concepts rather than mathematics, it covers a broad range of topics including neural networks, support vector machines, classification trees, and boosting. With many examples and color graphics, this is a valuable resource for statisticians and anyone interested in data mining in science or industry.
Machine Learning For Absolute Beginners book cover
Machine Learning For Absolute Beginners
A Plain English Introduction (Machine Learning From Scratch)
Oliver Theobald - 2017-02-18
Goodreads Rating
Learn the practical components and statistical concepts of machine learning with this clear and concise guide for absolute beginners. Written without the need for programming experience, you'll be introduced to core algorithms and visual examples to guide you through creating your first machine learning model using Python. The second edition includes new topics such as data scrubbing and ensemble modeling, making it an excellent starting point for those ready to step into the world of machine learning. This book is not a sequel and is a revamped version of the first edition, but with additional information.
Deep Learning with Python book cover
Deep Learning with Python
François Chollet - 2017-12-04
Goodreads Rating
Explore the revolutionary technology of deep learning in this groundbreaking book. Discover its application to various AI problems, from image and speech recognition to question answering and text classification. Learn how deep learning drives Facebook and Google's photo tagging systems, self-driving cars, and speech recognition systems. With its ability to solve machine perception problems, deep learning can automate tasks such as photo tagging, simply by feeding it with examples. This book is a must-read for anyone interested in the cutting-edge technology of artificial intelligence.
Machine Learning book cover
Machine Learning
A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Kevin P. Murphy - 2012-08-24
Goodreads Rating
This comprehensive textbook introduces readers to machine learning and how it can be used to automatically detect patterns in data and predict future data. The author uses a unified, probabilistic approach and covers important background topics such as probability, optimization, and linear algebra. Recent developments in the field are discussed, and the book is filled with color images and examples from biology, text processing, computer vision, and robotics. The book stresses a model-based approach and includes pseudo-code for important algorithms. Suitable for upper-level undergraduates and beginning graduate students.
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 - 2015-10-01
Goodreads Rating
Discover the exciting world of machine learning and how it powers innovative breakthroughs in technology. With the help of Python, learn the fundamentals of transforming data into knowledge and developing algorithms efficiently. This book covers problem-solving with a focus on classification, regression analysis, and clustering. Build your machine learning system for sentiment analysis and embed it into a web app for the world to see. Gain practical knowledge and improve your skillset with the best practices of machine learning.
Recommended by
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
Artificial Intelligence by Stuart Russell
Machine Learning with R by Brett Lantz
Advances in Financial Machine Learning by Marcos Lopez de Prado
Natural Language Processing with Python by Steven Bird
Understanding Machine Learning by Shai Shalev-Shwartz
Deep Reinforcement Learning Hands-On by Maxim Lapan
Deep Learning for Coders with fastai and PyTorch by Jeremy Howard
Make Your Own Neural Network by Tariq Rashid
Learning from Data by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin
Grokking Deep Learning by Andrew Trask
Neural Networks for Pattern Recognition by Christopher M. Bishop
Neural Smithing by Russell Reed
TensorFlow Machine Learning Cookbook by Nick McClure
Machine Learning by Stephen Marsland
The Book of Why by Judea Pearl
Generative Deep Learning by David Foster
Reinforcement Learning by Richard S. Sutton
Speech and Language Processing by Daniel Jurafsky
Probabilistic Graphical Models by Daphne Koller
AI and Machine Learning for Coders by Laurence Moroney
Practical Data Science with R by Nina Zumel
Bayesian Reasoning and Machine Learning by David Barber
Machine Learning by Sergios Theodoridis
Machine Learning and Data Science Blueprints for Finance by Hariom Tatsat, Sahil Puri, Brad Lookabaugh
Data Science from Scratch by Joel Grus
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
Paradigms of Artificial Intelligence Programming by Peter Norvig
R for Data Science by Hadley Wickham
Deep Learning Illustrated by Jon Krohn
Machine Learning Design Patterns by Valliappa Lakshmanan
Machine Learning for Algorithmic Trading by Stefan Jansen
Building Machine Learning Powered Applications by Emmanuel Ameisen
ApproachingAny Machine Learning Problem by Abhishek Thakur
Hands-On Deep Learning Algorithms with Python by Sudharsan Ravichandiran
TinyML by Pete Warden
Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili
Python Data Science Handbook by Jake Vanderplas
Data Smart by John W. Foreman
Forecasting by Rob Hyndman, George Athanasopoulos
Bayesian Data Analysis by Andrew Gelman
Forecasting by Rob J Hyndman
Machine Learning Engineering by Andriy Burkov
Computer Programming And Cyber Security for Beginners by Zach Codings
Machine Learning with Python by Oliver Theobald
Artificial Intelligence and Machine Learning for Business by Steven Finlay
Machine Learning with Python Cookbook by Chris Albon
Neural Network Design by Martin T Hagan
Deeper Learning by Monica Martinez
The Selling Revolution by DJ Sebastian
Machine Learning by Ethem Mining
Machine Learning in Finance by Matthew F. Dixon, Igor Halperin, Paul Bilokon
Machine Learning Pocket Reference by Matt Harrison
Foundations of Machine Learning by Mehryar Mohri
Grokking Machine Learning by Luis Serrano
Natural Language Processing in Action by Hobson Lane
Practical Natural Language Processing by Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, Harshit Surana
Neural Networks and Deep Learning by Charu C. Aggarwal
Deep Learning in Production by Sergios Karagiannakos
Real-World Machine Learning by Henrik Brink
Ultimate Step by Step Guide to Machine Learning Using Python by Daneyal Anis
Deep Learning by Michael Fullan
Undefined (The Elemental Saga) by Jessica Ruddick
Algorithmic Trading by Jeffrey M Bacidore