100 Best Machine Learning Books of All Time (Updated for 2026)

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1

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Geron

5.0

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 production-ready Python frameworks-scikit-learn and

Recommended by Mark Tabladillo.

Mark Tabladillo

Book to Start You on Machine Learning - KDnuggets https://t.co/19fdX59b0d This book is “Hands-On Machine Learning with Scikit-Learn & TensorFlow”. each new revision has become an even better version of one of the best in-depth resources to learn Machine Learning by doing. https://t.co/ujyUH3xU3e [source]

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2

Deep Learning

Ian Goodfellow, Yoshua Bengio, et al.

5.0

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 computer gathers knowledge from experience, there is no

Recommended by Elon Musk, Nassim Nicholas Taleb, Satya Nadella, and 2 others.

Elon Musk

Founder/SpaceX

Written by three experts in the field, Deep Learning is the only comprehensive book on the subject. [source]

Nassim Nicholas Taleb

Author

Very clear exposition, does the math without getting lost in the details. Although many of the concepts of the introductory first 100 pages can be found elsewhere, they are presented with remarkable cut-to-the-chase clarity. [source]

Satya Nadella

CEO/Microsoft

Elon Musk and Facebook AI chief Yann LeCun have praised this textbook on one of software’s most promising frontiers. After its publication, Microsoft signed up coauthor Bengio, a pioneer in machine learning, as an adviser [source]

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3

An Introduction to Statistical Learning: With Applications in R

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

4.9

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant a

Recommended by Roger D. Peng.

Roger D. Peng

This book is written by a powerhouse of authors in the machine learning community, true authorities in the field. But beyond that, they’re also great writers. [source]

4

Pattern Recognition and Machine Learning

Christopher M. Bishop

4.9

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, while graphical models have emerged as a general

5

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Trevor Hastie, Robert Tibshirani, Jerome Friedman

4.9

During the past decade there has been an explosion in computation and information technology. With it have 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 mining, machine learning, and bioinformatics. Ma

Recommended by Nassim Nicholas Taleb.

Nassim Nicholas Taleb

Author

Very comprehensive, sufficiently technical to get most of the plumbing behind machine learning. Very useful as a reference book (actually, there is no other complete reference book). The authors are the real thing (Tibshirani is the one behind the LASSO regularization technique). Uses some mathematical statistics without the burdens of measure theory and avoids the obvious but complicated proofs. I own two copies of this edition, one for the office, one for my house, and the authors generously provide the PDF for travelers like me. [source]

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6

The Hundred-Page Machine Learning Book

Andriy Burkov

4.8

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 these concepts.

Companion wiki — the book has a conti

Recommended by Kirk Borne.

Kirk Borne

Recent top-selling books in #AI & #MachineLearning: https://t.co/Ij9I7SzR4d ————— #BigData #DataScience #DataMining #Algorithms #PredictiveAnalytics #Python ————— ...in the TOP 10: 1)The Hundred-Page ML Book: https://t.co/dQ7nP6gwP0 2)Hands-on ML with...: https://t.co/Y0Iz3GbtGP https://t.co/72rAFN1FwW [source]

7

Superintelligence: Paths, Dangers, Strategies

Nick Bostrom

4.8

Preview Summary

Superintelligence asks the questions: what happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? Nick Bostrom lays the foundation for understanding the future of humanity and intelligent life.

The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant posi

Recommended by Elon Musk, Maria Ramos, and Will MacAskill.

Elon Musk

Founder/SpaceX

Worth reading Superintelligence by Bostrom. We need to be super careful with AI. Potentially more dangerous than nukes. [source]

Maria Ramos

CEO/Barclays Africa

Ramos will take the summer to examine some of the questions weighing more heavily on humankind as we contemplate our collective future: what happens when we can write our own genetic codes, and what happens when we create technology that is meaningfully more intelligent than us. The Gene: An Intimate History—Siddhartha Mukherjee Superintelligence: Paths, Dangers, Strategies—Nick Bostrom The Ministry of Utmost Happiness—Arundhati Roy [source]

Will MacAskill

I picked this book because the possibility of us developing human-level artificial intelligence, and from there superintelligence—an artificial agent that is considerably more intelligent than we are—is at least a contender for the most important issue in the next two centuries. Bostrom’s book has been very influential in effective altruism, lots of people work on artificial intelligence in order to ensure that it is developed safely. I don’t agree with the entire contents of the book, but there are many compelling arguments in it. It would be extremely overconfident to dismiss this book as too speculative. In fact, I think there should be a lot more work that tries to understand the biggest challenges for the next two hundred years, and what we could do to try to overcome them. [source]

8

Machine Learning: A Probabilistic Perspective

Kevin P. Murphy

4.8

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehen

Recommended by Kirk Borne.

Kirk Borne

#MachineLearning — a Probabilistic Perspective: https://t.co/wAZwLoUFGF ———— #BigData #Statistics #DataScience #DeepLearning #AI #Algorithms #StatisticalLiteracy #Mathematics #abdsc ——— ⬇Get this brilliant 1100-page 28-chapter highly-rated book: https://t.co/Tm2zchpHSu https://t.co/jprUDdzkj8 [source]

9

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition

Sebastian Raschka

4.8

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 from the field of machine learning, we can turn thi

10

Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems

Aurélien Géron

4.8

11

Life 3.0: Being Human in the Age of Artificial Intelligence

Max Tegmark

4.8

Preview Summary

How will Artificial Intelligence affect crime, war, justice, jobs, society and our very sense of being human? The rise of AI has the potential to transform our future more than any other technology--and there's nobody better qualified or situated to explore that future than Max Tegmark, an MIT professor who's helped mainstream research on how to keep AI beneficial.

How can we grow our prosperity th

Recommended by Barack Obama, Bill Gates, Elon Musk, and 2 others.

Barack Obama

Former USA President

As 2018 draws to a close, I’m continuing a favorite tradition of mine and sharing my year-end lists. It gives me a moment to pause and reflect on the year through the books I found most thought-provoking, inspiring, or just plain loved. It also gives me a chance to highlight talented authors – some who are household names and others who you may not have heard of before. Here’s my best of 2018 list. [source]

Bill Gates

CEO/Microsoft

Anyone who wants to discuss how artificial intelligence is shaping the world should read this book. [source]

Elon Musk

Founder/SpaceX

A compelling guide to the challenges and choices in our quest for a great future of life, intelligence and consciousness—on Earth and beyond. [source]

12

The Singularity is Near: When Humans Transcend Biology

Ray Kurzweil

4.7

Preview Summary

The great inventor and futurist Ray Kurzweil is one of the best-known and controversial advocates for the role of machines in the future of humanity. In his latest, thrilling foray into the future, he envisions an event--thesingularity--in which technological change becomes so rapid and so profound that our bodies and brains will merge with our machines.

The Singularity Is Near portrays what life w

Recommended by Tony Robbins, Anthony Robbins, Brian Armstrong, and 4 others.

Antonio Eram

This book was recommended by Antonio when asked for titles he would recommend to young people interested in his career path. [source]

Mark O'Connell

I wouldn’t be the first to look at him this way but I read Kurzweil’s work as essentially a work of religious mysticism. I think there’s no other way to read it, really. [source]

Steve Aoki

It opened me up to the idea of science fiction becoming science fact. [source]

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13

Deep Learning with Python

François Chollet

4.7

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 your smartphone, and much more.

In particular, Deep

14

Reinforcement Learning: An Introduction

Richard S. Sutton and Andrew G. Barto

4.7

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a comple

Recommended by Zachary Lipton.

Zachary Lipton

@innerproduct 1. Tor Lattimore Great book work on bandits (https://t.co/gttspSm40W) and work on causality + bandits (https://t.co/lkwvtEiKvE) 2. Caroline Uhler — Interesting work on causal inference + discovery, causal inference under measurement error etc (https://t.co/I3IRpwmdMd) [source]

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15

Introduction to Machine Learning with Python: A Guide for Data Scientists

Andreas C. Müller and Sarah Guido

4.6

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 learning applications are limited only by your ima

Recommended by Francesco Marconi.

Francesco Marconi

Top programming languages ranked by its annual search engine popularity. Python has gained momentum because of its importance to machine learning development. At @WSJ we are using it to build tools for journalists. Tip: this is a great book for anyone who wants to get started! https://t.co/ZsHjqB5gvC [source]

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16

Machine Learning

Tom M. Mitchell

4.6

Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

Recommended by Daniel H Wilson.

Daniel H Wilson

Yes, Machine Learning is a textbook and I would call it the textbook for machine learning and artificial intelligence. Machine learning is just the math of teaching a machine how to solve a problem on its own, because you’re not going to be able to be there to solve it for the machine. It can be any kind of problem: it could be a robot that needs to figure out how to get from point A to point B or it could be a supply inventory algorithm for trying to figure out how many products it should order for Walmart. [source]

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17

Artificial Intelligence: A Modern Approach

Stuart Russell and Peter Norvig

4.6

For one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Adde

18

Information Theory, Inference and Learning Algorithms

David J. C. MacKay

4.6

Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with appli

19

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

Pedro Domingos

4.6

A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own
In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil

Recommended by Vinod Khosla.

Vinod Khosla

Co-Founder/Sun Microsystem

If you want speculation about what the master AI might need (one view). For a slightly more technical read, I’d suggest Ian Goodfellows Deep Learning. [source]

20

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Cathy O'Neil

4.5

Preview Summary

Longlisted for the National Book Award
New York Times Bestseller

A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life -- and threaten to rip apart our social fabric

We live in the age of the algorithm. Increasingly, the decisions that affect our lives--where we go to school, whether we get a car loan, how much we pay for health insurance--are being made not

Recommended by Paula Boddington and Ramesh Srinivasan.

Paula Boddington

How the use of algorithms has affected people’s lives and occasionally ruined them. [source]

Ramesh Srinivasan

This book is a really fantastic analysis of how quantification, the collection of data, the modelling around data, the predictions made by using data, the algorithmic and quantifiable ways of predicting behaviour based on data, are all built by elites for elites and end up, quite frankly, screwing over everybody else. [source]

21

Learning From Data: A Short Course

Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin

4.5

Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we be

22

AI Superpowers: China, Silicon Valley, and the New World Order

Kai-Fu Lee

4.5

Dr. Kai-Fu Lee—one of the world’s most respected experts on AI and China—reveals that China has suddenly caught up to the US at an astonishingly rapid and unexpected pace. In AI Superpowers, Kai-fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected. Indeed, as the US-Sino AI competition begins to hea

Recommended by Arianna Huffington, Satya Nadella, Yuval Noah Harari, and 13 others.

Arianna Huffington

Founder/The Huffington Post

Kai-Fu Lee's experience as an AI pioneer, top investor, and cancer survivor has led to this brilliant book about global technology. AI Superpowers gives us a guide to a future that celebrates all the benefits that AI will bring, while cultivating what is unique about our humanity. It’s one of those books you read and think, ‘Why are people reading any other book right now when this is so clearly the one they need to be reading? [source]

Satya Nadella

CEO/Microsoft

Kai-Fu Lee's smart analysis on human-AI coexistence is clear-eyed and a must-read. We must look deep within ourselves for the values and wisdom to guide AI's development. [source]

Yuval Noah Harari

Historian

A superb and very timely survey of the impact of AI on the geopolitical system, the job market and human society. [source]

23

Data Science from Scratch: First Principles with Python

Joel Grus

4.5

To really learn data science, you should not only master the tools--data science libraries, frameworks, modules, and toolkits--but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.

If you have an aptitude for mathematics and some programmi

Recommended by Balaji S. Srinivasan and Thorsten Heller.

Thorsten Heller

The Best #book to Start your #DataScience Journey - Towards #DataScience https://t.co/D8PlkkSxw6 by @benthecoder1 [source]

24

Applied Predictive Modeling

Max Kuhn and Kjell Johnson

4.5

This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic

Recommended by Kirk Borne.

Kirk Borne

Find more than 40 useful #PredictiveModeling articles here at @DataScienceCtrl https://t.co/KdcvLRffRk #abdsc ———— #BigData #DataScience #AI #MachineLearning #Forecasting #Statistics #PredictiveAnalytics ——— +This is the best book on the subject: https://t.co/SmsepmniHi https://t.co/amBJHCJSHN [source]

25

Make Your Own Neural Network

Tariq Rashid

4.4

26

Probabilistic Graphical Models: Principles and Techniques

Daphne Koller, Nir Friedman

4.4

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.

Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The

27

Data Science for Business: What you need to know about data mining and data-analytic thinking

Foster Provost and Tom Fawcett

4.4

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

Based on an MBA course Pr

Recommended by Kirk Borne.

Kirk Borne

Great book for Business Analytics and for building #AnalyticThinking >> “#DataScience for Business — What You Need to Know about #DataMining and Data-Analytic Thinking”: https://t.co/e9rAFnVYYQ #BigData #MachineLearning #DataStrategy #AnalyticsStrategy #Algorithms https://t.co/yEblfU2MZd [source]

28

The Signal and the Noise: Why So Many Predictions Fail - But Some Don't

Nate Silver

4.4

Preview Summary

One of Wall Street Journal's Best Ten Works of Nonfiction in 2012

New York Times Bestseller

"Not so different in spirit from the way public intellectuals like John Kenneth Galbraith once shaped discussions of economic policy and public figures like Walter Cronkite helped sway opinion on the Vietnam War…could turn out to be one of the more momentous books of the decade."
-New York Times Book Review

"Na

Recommended by Bill Gates.

Bill Gates

CEO/Microsoft

Anyone interested in politics may be attracted to Nate Silver’s The Signal and the Noise: Why So Many Predictions Fail—but Some Don't. Silver is the New York Times columnist who got a lot of attention last fall for predicting—accurately, as it turned out–the results of the U.S. presidential election. This book actually came out before the election, though, and it’s about predictions in many domains besides politics. Silver knows a lot about baseball, and I especially liked his explanation of hold’em poker. A few pages – where he talks about how early computers supposedly made everything less efficient—are utter nonsense. I wish he had gone into more depth on some things, like why it is that voters are increasingly polarized. I liked the book, though I wish he’d gone deeper on a number of topics. [source]

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29

Programming Collective Intelligence: Building Smart Web 2.0 Applications

Toby Segaran

4.4

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect da

30

Understanding Machine Learning: From Theory to Algorithms

Shai Shalev-Shwartz, Shai Ben-David

4.4

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into

31

Pattern Classification

Richard O. Duda, Peter E. Hart, David G. Stork

4.4

The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and

Recommended by Eric Weinstein.

Eric Weinstein

Managing Director/Thiel Capital

Eric Weinstein recommended this book on Twitter. [source]

32

Gödel, Escher, Bach: An Eternal Golden Braid

Douglas R. Hofstadter

4.4

Douglas Hofstadter's book is concerned directly with the nature of “maps” or links between formal systems. However, according to Hofstadter, the formal system that underlies all mental activity transcends the system that supports it. If life can grow out of the formal chemical substrate of the cell, if consciousness can emerge out of a formal system of firing neurons, then so too will computers at

Recommended by Steve Jurvetson, Seth Godin, Cory Zue, and 2 others.

Steve Jurvetson

Steve Jurvetson recommended this book on the podcast "The Tim Ferriss Show". [source]

Seth Godin

Author, Marketer, Entrepreneur

In the last week, I discovered that at least two of my smart friends hadn't read Godel, Escher, Bach. They have now. You should too. [source]

Cory Zue

More recently books like Godel, Escher, Bach and Sapiens have influenced my thinking a lot. [source]

33

Human Compatible: Artificial Intelligence and the Problem of Control

Stuart Russell

4.4

A leading artificial intelligence researcher lays out a new approach to AI that will enable us to coexist successfully with increasingly intelligent machines

In the popular imagination, superhuman artificial intelligence is an approaching tidal wave that threatens not just jobs and human relationships, but civilization itself. Conflict between humans and machines is seen as inevitable and its out

Recommended by Elon Musk, Diane Coyle, and Marcus Borba.

Elon Musk

Founder/SpaceX

Worth reading. [source]

Diane Coyle

there’s a whole clutch of AI books…People want to understand what’s going on. Human Compatible is a really clearly written one. It explains enough about how AI works, but also what some of the challenges are. [source]

Marcus Borba

Book Review, ‘Human Compatible’: A Book About Artificial Intelligence (#AI) That Asks Some Interesting Questions https://t.co/BCe5JnHPuE @Forbes #ArtificialIntelligence #DataScience #BigData #DeepLearning #Robotics #MachineLearning https://t.co/gKo0mpBeva [source]

34

The Book of Why: The New Science of Cause and Effect

Judea Pearl

4.4

A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence
"Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleag

Recommended by D.a. Wallach and Kirk Borne.

D.a. Wallach

@EricTopol @yudapearl @bschoelkopf @MPI_IS I love @yudapearl 's book so much! Profound, heterodox. [source]

Kirk Borne

.@yudapearl wrote the awesome "Book of Why", but he recommends this fun and less #mathematics-heavy read >> his #AI lecture given in 1999: https://t.co/kNYIoJ8qcY #DataScience #MachineLearning #Statistics #BookofWhy #Causalinference #Bayes https://t.co/CNQlKP8cU3 [source]

35

Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are

Seth Stephens-Davidowitz

4.4

Preview Summary

Foreword by Steven Pinker

Blending the informed analysis of The Signal and the Noise with the instructive iconoclasm of Think Like a Freak, a fascinating, illuminating, and witty look at what the vast amounts of information now instantly available to us reveals about ourselves and our world—provided we ask the right questions.

By the end of an average day in the early twenty-first century, human bei

Recommended by Jj. Omojuwa and Ron Fournier.

Jj. Omojuwa

@SympLySimi Lol. Read this book. You’d love it. https://t.co/d2cLOyoiZ9 [source]

Ron Fournier

Just finished, “Everybody Lies” by @SethS_D, which in addition to being a tremendous education on Big Data, includes the best conclusion to a non-fiction book I’ve ever read. Read it. -30- [source]

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36

Python Data Science Handbook: Tools and Techniques for Developers

Jake VanderPlas

4.4

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

Working scientists and data crunchers famili

Recommended by Kirk Borne and 1 others.

Kirk Borne

✨🎉🌟Must see this >> Free #Python #DataScience Coding book series for #DataScientists ...via @DataScienceCtrl Go to https://t.co/To10VVZzIl ——————— #abdsc #BigData #MachineLearning #AI #DeepLearning #BeDataBrilliant #DataLiteracy https://t.co/Msuo1jiZSm [source]

37

Algorithms to Live By: The Computer Science of Human Decisions

Brian Christian and Griffiths

4.4

Preview Summary

A fascinating exploration of how insights from computer algorithms can be applied to our everyday lives, helping to solve common decision-making problems and illuminate the workings of the human mind

All our lives are constrained by limited space and time, limits that give rise to a particular set of problems. What should we do, or leave undone, in a day or a lifetime? How much messiness should we

Recommended by Doug McMillon, Chris Oliver, Emi Gal, and 2 others.

Doug McMillon

CEO/Walmart

Here are some of my favorite reads from 2017. Lots of friends and colleagues send me book suggestions and it's impossible to squeeze them all in. I continue to be super curious about how digital and tech are enabling people to transform our lives but I try to read a good mix of books that apply to a variety of areas and stretch my thinking more broadly. [source]

Chris Oliver

This is a great book talking about how you can use computer science to help you make decisions in life. How do you know when to make a decision on the perfect house? Car? etc? It helps you apply algorithms to making those decisions optimally without getting lost. [source]

Emi Gal

When asked what books he would recommend to young people interested in his career path, mentioned Algorithms to Live By. [source]

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38

Mining of Massive Datasets

Anand Rajaraman, Jeffrey David Ullman

4.3

The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms auto

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39

Neural Networks and Deep Learning

Charu C. Aggarwa

4.3

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 recognit

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40

Bayesian Reasoning and Machine Learning

David Barber

4.3

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to

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48

On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines

Jeff Hawkins, Sandra Blakeslee

4.3

From the inventor of the PalmPilot comes a new and compelling theory of intelligence, brain function, and the future of intelligent machines

Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intellige

Recommended by Ev Williams and Joan Boixados.

Joan Boixados

I’m reading “On intelligence” by Jeff Hawkins. I am really enjoying it. It’s a very specific theory of how our brain learns and makes predictions (the root of our intelligence) explained for average people unfamiliar with the field. It’s also very related to computer science and artificial intelligence since it tried to prove the current approaches to those are flawed. I’m getting a better understanding of how our brain works and how does our behaviour affects our thinking as much as our thinking affects our behaviour. Thus, it’s interesting to connect it with the idea of habits and how we can really benefit from them. [source]

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49

Machine Learning Yearning

Andrew Ng

4.3

AI, machine learning, and deep learning are transforming numerous industries. But building a machine learning system requires that you make practical decisions:

Should you collect more training data?
Should you use end-to-end deep learning?
How do you deal with your training set not matching your test set?
and many more.

Historically, the only way to learn how to make these "strategy" decisions ha

50

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies

Erik Brynjolfsson

4.3

A revolution is under way. In recent years, Google's autonomous cars have logged thousands of miles on American highways and IBM's Watson trounced the best human Jeopardy! players. Digital technologies with hardware, software, and networks at their core will in the near future diagnose diseases more accurately than doctors can, apply enormous data sets to transform retailing, and accomplish many t

Recommended by Michael Dell, Vinod Khosla, Vinod Khosla, and 1 others.

Michael Dell

CEO/Dell Computers

The authors make a case for a future world that is better, not worse, than the one we inherited. That may seem far-fetched given the problems we see flashing across our screens every day. But there is reason for optimism, and it starts and ends with one of my favorite things, technology. [source]

Dominic Steil

One of the books that had the biggest impact on . [source]

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51

Data Smart: Using Data Science to Transform Information into Insight

JOHN W. FOREMAN

4.3

Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.

But how does one exactly do data science? Do you have to hire one of these prie

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52

Machine Learning For Absolute Beginners: A Plain English Introduction

Oliver Theobald

4.2

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 principles to march through first. But rather than spen

53

Natural Language Processing with Python

Steven Bird, Ewan Klein, Edward Loper

4.2

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range

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54

Bayesian Data Analysis

Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin

4.2

Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, base

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55

Prediction Machines: The Simple Economics of Artificial Intelligence

Ajay Agrawal, Joshua Gans, Avi Goldfarb

4.2

"What does AI mean for your business? Read this book to find out." -- Hal Varian, Chief Economist, Google

Artificial intelligence does the seemingly impossible, magically bringing machines to life--driving cars, trading stocks, and teaching children. But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan thei

Recommended by Lawrence Summers, Dominic Barton, Kevin Kelly, and 1 others.

Lawrence Summers

Chief Economist/World Bank

AI may transform your life. And Prediction Machines will transform your understanding of AI. This is the best book yet on what may be the best technology that has come along. [source]

Dominic Barton

CEO/McKinsey

Prediction Machines achieves a feat as welcome as it is unique: a crisp, readable survey of where artificial intelligence is taking us separates hype from reality, while delivering a steady stream of fresh insights. It speaks in a language that top executives and policy makers will understand. Every leader needs to read this book. [source]

Kevin Kelly

This book makes artificial intelligence easier to understand by recasting it as a new, cheap commodity--predictions. It's a brilliant move. I found the book incredibly useful. [source]

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56

Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition

Brett Lantz

4.2

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science ta

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57

Advances in Financial Machine Learning

Marcos López de Prado

4.2

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; h

58

Naked Statistics: Stripping the Dread from the Data

Charles Wheelan

4.2

Preview Summary

Once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called “sexy.” From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which m

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59

Neural Networks for Pattern Recognition

Christopher M. Bishop

4.2

This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, princi

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60

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

John D. Kelleher, Brian Mac Namee, Aoife D'Arcy

4.2

A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting cu

61

Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition

Daniel Jurafsky, James H. Martin

4.2

This book offers a unified vision of speech and language processing, presenting state-of-the-art algorithms and techniques for both speech and text-based processing of natural language. This comprehensive work covers both statistical and symbolic approaches to language processing; it shows how they can be applied to important tasks such as speech recognition, spelling and grammar correction, infor

62

Code: The Hidden Language of Computer Hardware and Software

Charles Petzold

4.2

What do flashlights, the British invasion, black cats, and seesaws have to do with computers? In CODE, they show us the ingenious ways we manipulate language and invent new means of communicating with each other. And through CODE, we see how this ingenuity and our very human compulsion to communicate have driven the technological innovations of the past two centuries.

Using everyday objects and fam

Recommended by Ana Bell.

Ana Bell

It gets you to use your imagination to virtually build a computer. It’s easy to read, you can lie down on the couch and enjoy it—it’s not so much of a textbook. It demystifies the magic of a computer and what it is. [source]

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63

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

Chris Albon

4.2

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topi

Recommended by Dj Patil and Kirk Borne.

Dj Patil

Because @chrisalbon is too humble to promote his book, I'm going to step in and say you should really go out and get it. Built on top of his awesome flash cards (https://t.co/ZbvZumC9Kq). It's a great way to get going on machine learning https://t.co/G3kKKErgIb [source]

Kirk Borne

✨🎉🌟Must see this >> Free #Python #DataScience Coding book series for #DataScientists ...via @DataScienceCtrl ——————————————— #abdsc #Coding #MachineLearning #BigData #BeDataBrilliant #DataLiteracy 👇👇👇 https://t.co/l14zcnYlb7 [source]

64

Foundations of Machine Learning

Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar

4.2

Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms.

This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The a

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65

Introduction to Machine Learning

Ethem Alpaydin

4.2

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from

66

Hello World: Being Human in the Age of Algorithms

Hannah Fry

4.2

A look inside the algorithms that are shaping our lives and the dilemmas they bring with them.

If you were accused of a crime, who would you rather decide your sentence—a mathematically consistent algorithm incapable of empathy or a compassionate human judge prone to bias and error? What if you want to buy a driverless car and must choose between one programmed to save as many lives as possible and

Recommended by David Smith and Jim Al-Khalili.

David Smith

Darroch: “The best book I’ve read recently is called Hello World... It’s about the impact of algorithms across different areas... For me this was the best piece of learning I’ve done in recent months.” [source]

Jim Al-Khalili

The fact is, the age of AI is coming fast, and we need to be ready for it. This book will help you decide how worried you should be. [source]

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67

Real-World Machine Learning

Henrik Brink, Joseph Richards, Mark Fetherolf

4.2

Summary

Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats

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68

Doing Bayesian Data Analysis: A Tutorial Introduction with R

John K. Kruschke

4.1

There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained i

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69

Computer Age Statistical Inference

Bradley Efron, Trevor Hastie

4.1

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating jour

70

Storytelling with Data: A Data Visualization Guide for Business Professionals

Cole Nussbaumer Knaflic

4.1

Don't simply show your data--tell a story with it!Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples--ready for im

Recommended by Roger D. Peng.

Roger D. Peng

It’s important to think in terms of what your audience needs, and what would be best for them among the many choices you could make when analysing data. [source]

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71

Probability Theory

E. T. Jaynes, G. Larry Bretthorst

4.1

Going beyond the conventional mathematics of probability theory, this study views the subject in a wider context. It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an a

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72

Building Machine Learning Systems with Python

Willi Richert, Luis Pedro Coelho

4.1

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73

Machine Learning in Action

Peter Harrington

4.1

The ability to take raw data, access it, filter it, process it, visualize it, understand it, and communicate it to others is possibly the most essential business problem for the coming decades. "Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw dat

74

Rebooting AI: Building Artificial Intelligence We Can Trust

Gary Marcus, Ernest Davis

4.1

Preview Summary

Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a truly robust AI.

Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we are led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and hav

75

Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence

Jon Krohn, Grant Beyleveld, et al.

4.1

"The authors' clear visual style provides a comprehensive look at what's currently possible with artificial neural networks as well as a glimpse of the magic that's to come."
--Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning

Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented alg

Recommended by Kirk Borne.

Kirk Borne

🌟📘📊📈Awesome new book >> #DeepLearning Illustrated — A Visual, Interactive Guide to Artificial Intelligence” https://t.co/xIW48MskrR by @JonKrohnLearns ——————— #BigData #Analytics #DataScience #AI #MachineLearning #Algorithms #NeuralNetworks https://t.co/JKSrVRLpS0 [source]

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76

Elements of Information Theory

Thomas M. Cover and Joy A. Thomas

4.1

The latest edition of this classic is updated with new problem sets and material

The Second Edition of this fundamental textbook maintains the book's tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory.

All the essential topics in information theory are covered in detail, including ent

Recommended by Eric Weinstein and Nassim Nicholas Taleb.

Eric Weinstein

Managing Director/Thiel Capital

Folks frequently ask “What are the books that changed your life?” If I tell them, they are usually radically disappointed. I find that curious. I just cleared out of an office, and these are 4 shelves of spines of books that mattered enough to me to bring home. So here they are. [source]

Janelle Shane

4.0

AS HEARD ON NPR'S "SCIENCE FRIDAY"
Discover the book that Malcolm Gladwell, Susan Cain, Daniel Pink, and Adam Grant want you to read this year, an "accessible, informative, and hilarious" introduction to the weird and wonderful world of artificial intelligence (Ryan North).
"You look like a thing and I love you" is one of the best pickup lines ever... according to an artificial intelligence traine

Recommended by Robert Went.

Robert Went

Reading ‘You look like a thing and I love you: How AI works and why it is making the world a weirder place’, a wonderful book by @JanelleCShane — very funny, and I learn a lot https://t.co/SaZPjRTdVw [source]

87

Design Patterns: Elements of Reusable Object-Oriented Software

Erich; Helm John Gamma

4.0

Capturing a wealth of experience about the design of object-oriented software, four top-notch designers present a catalog of simple and succinct solutions to commonly occurring design problems. Previously undocumented, these 23 patterns allow designers to create more flexible, elegant, and ultimately reusable designs without having to rediscover the design solutions themselves.

The authors begin by

88

Automate the Boring Stuff with Python: Practical Programming for Total Beginners

Al Sweigart

4.0

If you've ever spent hours renaming files or updating hundreds of spreadsheet cells, you know how tedious tasks like these can be. But what if you could have your computer do them for you?

In "Automate the Boring Stuff with Python," you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand no prior programming experience required. Once you've master

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89

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Nello Cristianini, John Shawe-Taylor

4.0

This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in a

90

Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & Tensorflow

Anirudh Koul, Siddha Ganju, et al.

4.0

Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.

Relying on years of indu

91

The Emperor's New Mind: Concerning Computers, Minds, and the Laws of Physics (Oxford Landmark Science)

Roger Penrose

4.0

For many decades, the proponents of `artificial intelligence' have maintained that computers will soon be able to do everything that a human can do. In his bestselling work of popular science, Sir Roger Penrose takes us on a fascinating tour through the basic principles of physics, cosmology, mathematics, and philosophy to show that human thinking can never be emulated by a machine.

Oxford Landmark

Recommended by Jim Al-Khalili.

Jim Al-Khalili

If things are left to their own devices they decay and unwind and disorder increases. If you take a pack of cards in the right order and shuffle it they will get mixed up. [source]

92

If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All

Eliezer Yudkowsky, Nate Soares

4.0

INSTANT NEW YORK TIMES BESTSELLER | The New Yorker's Best Books of 2025 | The Guardian's Best Books of 2025 | A 2025 Booklist Editors' Choice Pick The scramble to create superhuman AI has put us on the path to extinction—but it’s not too late to change course, as two of the field’s earliest researchers explain in this clarion call for humanity. "May prove to be the most important book of our time.

93

Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins

Garry Kasparov

4.0

Garry Kasparov's 1997 chess match against the IBM supercomputer Deep Blue was a watershed moment in the history of technology. It was the dawn of a new era in artificial intelligence: a machine capable of beating the reigning human champion at this most cerebral game.
That moment was more than a century in the making, and in this breakthrough book, Kasparov reveals his astonishing side of the sto

Recommended by Walter Isaacson, Charles Duhigg, Max Levchin, and 1 others.

Walter Isaacson

Author

The great Garry Kasparov takes on the key economic issue of our time: how we can thrive as humans in a world of thinking machines. This important and optimistic book explains what we as humans are uniquely qualified to do. Instead or wringing our hands about robots, we should all read this book and embrace the future. [source]

Charles Duhigg

Author

Garry Kasparov's perspectives on artificial intelligence are borne of personal experience - and despite that, are optimistic, wise and compelling. It's one thing for the giants of Silicon Valley to tell us our future is bright; it is another thing to hear it from the man who squared off with the world's most powerful computer, with the whole world watching, and his very identity at stake. [source]

Peter Flac

4.0

As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of

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100

Artificial Intelligence: A Guide for Thinking Humans

Melanie Mitchell

4.0

A sweeping examination of the current state of artificial intelligence and how it is remaking our world

No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent

Recommended by David Shaywitz.

David Shaywitz

Really enjoying, and have nearly completed (via @audible_com) @MelMitchell1 fascinating new book on AI. https://t.co/feb6t8qwMJ highly recommended! And h/t to @kevinhorgan for yet another splendid suggestion. [source]