Our Favorite Artificial Intelligence Books

Artificial intelligence is a vast discipline and only growing. At AIFWD, we pride ourselves in recommending educational programs — bootcamps, bachelor’s programs, master’s programs, and Ph.D. programs — to help interested individuals break in and level up in the field.

But while we believe that formal education is the surest way to a career in artificial intelligence, it’s by no means the only way to bone up on AI and machine learning. Indeed, even in our increasingly digital world books remain an essential resource, whether you’re considering a career in artificial intelligence and eager to learn more, in an AI degree program and looking to supplement your knowledge, already an AI professional and hoping to add new techniques to your toolbox, or just want to better understand how artificial intelligence and machine learning are impacting our economic, political, and social realities.

In the list below, we’ve assembled some of our favorite artificial intelligence books for beginners, experts, and everyone in between.

All prices were current on Amazon at the time of writing.

If you’re just beginning your artificial intelligence journey, these books will get you up to speed with basic concepts and skills like machine learning and Python programming.

Artificial Intelligence: A Modern Approach, 4th Edition. By Peter Norvig and Stuart Russell. Pearson; 1152 pages; $170.66 on Amazon

We begin with the standard textbook for anyone looking to break into artificial intelligence or machine learning, written by Google’s former director of research and search quality (Norvig) and a world-renowned Berkeley computer science professor (Russell). Within, you’ll find up-to-date overviews of some of the most important topics in the field, including deep learning, transfer learning, reinforcement learning, and natural language processing. While it might not get you designing your own AI systems from scratch, if you want an authoritative account of the theory behind artificial intelligence and machine learning practice, this is your book. And if you don’t want to take our word for it, trust the hundreds of professors who assign it to their elementary machine learning and artificial intelligence students.

Machine Learning: A Probabilistic Perspective. By Kevin P. Murphy. The MIT Press; 1104 pages; $104.42 on Amazon

Many students’ first introduction to AI, Kevin Murphy’s textbook offers students a unified approach to machine learning and its application in the world of big data through probabilistic models. Topics covered include unsupervised learning, supervised learning, reinforcement learning, and deep learning. All of the methods taught in the text are also implemented in a MATLAB software package offered for free online. For the best experience, readers should already have a foundational understanding of college-level mathematics.

Introduction to Machine Learning with Python: A Guide for Data Scientists. By Andreas C. Müller and Sarah Guido. O’Reilly Media; 398 pages; $53.62 on Amazon

Andreas Müller and Sara Guido’s introduction to machine learning with Python is great for those looking to get smart on artificial intelligence and machine learning to work in data science, but it’s also a valuable resource for future AI and machine learning engineers. Müller and Guido emphasize the practical machine learning tools needed to succeed in the industry, offering step-by-step guidance to help even AI novices install essential Python libraries and tools and start writing machine learning algorithms. What the authors don’t include, however, is much explanation of the math behind these algorithms, instead recommending readers turn to resources like Trevor Hastie, Robert Tibshirani, and Jerome Friedman’s The Elements of Statistical Learning (see below).

The Elements of Statistical Learning: Data Mining, Inference, and Prediction. By Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Springer; 767 pages; $73.57 on Amazon

The Elements of Statistical Learning offers a crucial overview of the mathematics at the heart of unsupervised and supervised learning, with an emphasis on data mining. Contained within are detailed explanations of the statistical concepts behind topics like regression, Bayesian reasoning, and neural networks. Though the book can be read straight through, it will be most effective as a reference to add context alongside other studies.

These artificial intelligence books contain theoretical and practical information relating to intermediate and advanced technical topics like deep learning, computer vision, and natural language processing.

Computer Vision: Algorithms and Applications. By Richard Szeliski. Springer; 947 pages; $53.99 on Amazon

This leading computer vision textbook, written by a computer vision researcher at Facebook and Microsoft Research, is a go-to reference for advanced artificial intelligence and machine learning students who want to better understand the theory and algorithms required for specific computer vision use cases. Topics covered include image classification, feature detection, computational photography, depth estimation, and image-based rendering.

Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images. By Valliapa Lakshmanan, Martin Görner, and Ryan Gillard. O’Reilly Media; 482 pages; $52.99 on Amazon

For those looking for a more practical guide to computer vision with an emphasis on programming, this O’Reilly volume written by three Google engineers offers crucial information on how to design machine learning systems for computer vision. Readers are led step-by-step through model selection, creating data pipelines for training, preprocessing images, deploying models, and maintaining AI systems, all within the TensorFlow and Keras Python machine learning libraries. 

Speech and Language Processing. By Daniel Jurafsky and James H. Martin. Prentice Hall; 1032 pages; $219.00 on Amazon

Written by linguistics and computer science professors at Stanford and Colorado–Boulder, Speech and Language Processing is the first comprehensive natural language processing textbook. Advanced artificial intelligence and machine learning students with eyes on the industry will particularly benefit from this text, as it supports its theoretical discussions with an analysis of the real-world application of NLP in large companies. With an emphasis on English, the text is structured by linguistic elements and concepts like “Words,” “Speech,” “Syntax,” and “Semantics,” with relevant machine-learning techniques and applications provided for each.

Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. By Steven Bird, Ewan Klein, and Edward Loper. O’Reilly Publishing; 502 pages; $49.69 on Amazon 

Those looking for a programming guide for natural language programming should consider this O’Reilly volume. In it, the authors provide step-by-step instructions for how to write NLP Python programs that can pull information from unstructured textual data, analyzing linguistic structure, categorize and tag words, and classify text. Readers will also become acquainted with common NLP software packages like the Natural Language Toolkit (NLTK) open-source library.

Deep Learning with Python. By François Chollet. Manning. 504 pages. $39.49 on Amazon

If you already have a handle on AI basics and want to get started learning how to design deep learning models, Chollet’s ground-up introduction is for you. Chollet offers a clear and deliberate path into coding neural networks with Keras — the software library he created — beginning first with a conceptual overview of machine learning and deep learning, then focusing on the relevant mathematics and software, and finally offering instructions for how to code a variety of deep learning techniques and applications, including recurrent neural networks (RNNs) for natural language processing and generative adversarial networks (GANs). Chollet closes the body of the text with best practices for the real world.

Deep Learning. By Ian Goodfellow, Yoshua Bengio, and Aaron Courville. MIT Press. 800 pages. $73.18 on Amazon, free online

Less focused on specific coding environments than Chollet’s deep learning book, in Deep Learning, Goodfellow, Bengio, and Courville provide a classroom-ready guide to deep learning beginning with relevant applied mathematics (linear algebra, probability, and information theory), moving through deep learning basics (neural network architecture, training, optimization) and applications (computer vision, speech recognition, natural language processing), before culminating with the latest topics in deep learning research (autoencoders, representation learning, deep generative models). 

Pattern Recognition and Machine Learning. By Christopher M. Bishop. Springer; 738 pages; $109.99 on Amazon

In Pattern Recognition and Machine Learning, Christopher Bishop, a laboratory director at Microsoft and professor of computer science at the University of Edinburgh, uses probability theory, decision theory, and information theory to introduce various machine learning techniques for pattern recognition. Techniques and concepts include reinforcement learning, decision trees, combined machine learning models, and Bayesian neural networks. While readers only need a knowledge of multivariable calculus and basic linear algebra to be able to follow along, Bishop’s writing is dense and technical, suited for those with significant experience with applied mathematics, computer science, and machine learning.

These books focus on how managers and executives can leverage artificial intelligence and machine learning to drive growth in business.

Age of Invisible Machines: A Practical Guide to Creating a Hyper Automated Ecosystem of Intelligence Digital Workers. By Robb Wilson. Wiley. 288 pages. $24.33 on Amazon

In this Wall Street Journal best seller, technologist Robb Wilson offers actionable strategies that business executives can put into practice to reinvigorate their companies and put themselves on a path toward hyper-automation. At the center of the book is conversational AI, a natural language processing technology reliant on machine learning. Throughout his book, Robb shares learnings from his experience at the helm of the conversational AI platform OneReach.ai on how business leaders can implement hyper-automation solutions to increase efficiency, cut expenses, and provide better value to users.

Prediction Machines: The Simple Economics of Artificial Intelligence. By Ajay Agrawal, Joshua Gans, and Avi Goldfarb. Harvard Business Review Press; 272 pages; $30.00 on Amazon

In Prediction Machines, three economists working at the University of Toronto’s Rotman School of Management simplify discussions around the rise of AI and machine learning in business by recasting this rise as a lowering of the cost of predictive data analytics. In so doing, they cut through the noise to demonstrate how machine learning can remove uncertainty and increase productivity. Throughout, the authors complement their incisive analyses with actionable steps for businesses to take to get more out of machine learning.

Power and Prediction: The Disruptive Economics of Artificial Intelligence. By Ajay Agrawal, Joshua Gans, and Avi Goldfarb. Harvard Business Review Press. 288 pages; $27.00 on Amazon

In this sophomore effort from the authors of Prediction Machines, they turn to focus squarely on data-driven decision-making. Breaking decision-making into prediction and judgment, they discuss how the process changes once businesses can (as they discuss in their first book) outsource prediction to artificial intelligence. Alongside their vivid examples, the authors offer practical suggestions to managers and executives for how to reorganize decision-making processes at a systems level to weather future AI disruptions. 

Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems. By Bernard Marr. Wiley; 352 pages; $24.06 on Amazon

For business leaders looking to implement AI solutions at their own companies, Bernard Marr’s 50 case studies provide ample inspiration. In each case study, Marr provides context, defines the specific problem the company faced, and analyzes why the AI solution was successful. Companies included in the book include Google, Amazon, Facebook, Tencent, McDonald’s, the Walt Disney Company, and GE.

These artificial intelligence books focus on the impact AI and machine learning are having on economies, societies, and politics across the globe.

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. By Cathy O’Neil. Crown; 272 pages; $14.39 on Amazon

With accessible case studies drawn from public school systems, political campaigns, baseball, and the job market, data scientist and blogger Cathy O’Neil presents her theory of “weapons of math destruction,” or WMDs: big data models encoded with bias, prejudice, or ignorance that do far more harm than good. A crucial (and early) intervention in the blind faith sometimes accorded big data and data mining, O’Neil’s writing is as enjoyable to read as it is alarming.

The Alignment Problem: Machine Learning and Human Values. By Brian Christian. W. W. Norton & Company; 496 pages; $28.95 on Amazon

Programmer and writer Brian Christian dives into the fallibility of algorithmic automation and decision-making, examining machine learning’s tendency to perpetuate bias, inequity, and other types of social harm through thematic lenses like “transparency,” “fairness,” and “inference.” Christian also offers an interesting insight into the steps already being taken by so-called “first responders” to remedy algorithmic unfairness and realign artificial with human values.

The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future. By Orly Lobel. PublicAffairs; 368 pages; $29.49 on Amazon

Tech policy scholar Orly Lobel offers a more optimistic view than those held by O’Neil and Christian. In The Equality Machine — an Economist Best Book of 2022 — she explores the ways that machine learning and other digital technologies, rather than increasing inequality and other social and environmental ills, are in some cases best suited to diagnose and address them. Drawing on diverse examples — from eBay’s disparity detection algorithm to  “marriage markets” — Lobel makes a powerful argument for approaching algorithmic responsibility questions with nuance and not sacrificing progress in pursuit of perfection.

Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. By Kate Crawford. Yale University Press; 336 pages; $20.99 on Amazon

In Atlas of AI, Microsoft researcher, and USC Annenberg professor Kate Crawford reckons with the environmental and political impacts of machine learning, revealing to readers the raw materials and labor capital  — and their sometimes questionable sources — needed to power every single algorithm written by machine learning engineers and data scientists. This is an especially good read for those considering entering machine learning or data science who want to better understand their work in a global context.

The Age of AI: And Our Human Future. By Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher. Back Bay Books; 288 pages; $18.99 on Amazon

In this national bestseller, leading thinkers of this century and last zoom out to examine artificial intelligence and machine learning within the context of human history: how they, like other technologies, have influenced how we think, how they’re reshaping the global network and global security, and where we might expect things to go from here. Along the way, they ponder fascinating questions, including how machine learning changes the nature of global conflict, what kinds of relationships (friendly or otherwise) we might strike up with AIs, where how AIs perceive the world might differ from our own perceptions, and, ultimately, how artificial intelligence might fundamentally alter what it means to be human

AI Superpowers: China, Silicon Valley, and the New World Order. By Kai-Fu Lee. Mariner Books; 288 pages; $10.99 on Amazon

In AI Superpowers, Sinovation Ventures CEO and former Microsoft and Apple executive Kai-Fu Lee offers an account of an AI future that sees China emerge as a new technological superpower, with massive implications not just for the blue-collar jobs traditionally thought to be put at risk by AI-driven automation, but white collar jobs as well. How, he ultimately asks, might we learn to coexist with AI as it continues gaining capabilities? — with universal basic income being among the possibilities he considers.

Life 3.0: Being Human in the Age of Artificial Intelligence. By Max Tegmark. Vintage; 384 pages; $13.99 on Amazon

MIT professor and president of the Future of Life Institute Max Tegmark offers a reader-friendly dive into the nature of artificial intelligence, the status of AI technologies today, and some of the most pressing questions about AI’s future. Central to the book is the question of whether we might soon live in a world with machines that possess superhuman intelligence. What might this world look like? What would the benefits be for our daily lives and societies? And what kinds of risks would there be — not just for individuals, but for our species as a whole?

Superintelligence: Paths, Dangers, Strategies. By Nick Bostrom. Oxford University Press; 390 pages; $14.36 on Amazon

Nick Bostrom’s New York Times bestseller tackles the same question as Tegmark does in Life 3.0 — namely, what does a future of superintelligent machines mean for humans — but in greater depth and detail with a less optimistic perspective. “As the fate of the gorillas now depends more on us humans than on the gorillas themselves,” he writes in his preface, “so the fate of our species would depend on the actions of the machine superintelligence.” While those already conversant in the basics of AI won’t have difficulty keeping up with it, those without an AI foundation will likely want to read Tegmark before diving into Bostrom’s counterpoint.

Conclusion

Reading notable and well-respected books on AI is an inexpensive, flexible way to boost your knowledge of the field. Whether you're looking to gain a foundational understanding of its core concepts, learn about cutting-edge consumer-facing or B2B technologies, or understand AI’s ethical and societal implications and tradeoffs, one of the excellent books above can help you move forward just as AI explodes in popularity.