Key Takeaways
- ✓Books remain a flexible way to build AI literacy alongside formal programs, bootcamps, certificates, and professional experience.
- ✓Beginners can start with broad AI and machine learning texts before moving into math-heavy or programming-heavy references.
- ✓Advanced readers can use specialized books to deepen skills in computer vision, natural language processing, deep learning, and pattern recognition.
- ✓Business and policy books can help readers understand how AI changes decision-making, organizations, labor markets, fairness, and risk.
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.
“The right AI book depends on whether you need foundations, implementation practice, leadership context, or a clearer view of AI’s broader consequences.”— AIFwD Editorial Staff
Best for Beginners
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.
Intermediate and Advanced Technical Topics
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.
AI and ML in Business
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.
Frequently Asked Questions
What is a good first artificial intelligence book for beginners?+
Beginners often benefit from a broad AI overview such as Artificial Intelligence: A Modern Approach, then a practical programming-focused book such as Introduction to Machine Learning with Python.
Do I need advanced math before reading AI books?+
Not for every book. Practical introductions may be accessible with basic programming experience, while books such as Machine Learning: A Probabilistic Perspective, The Elements of Statistical Learning, and Pattern Recognition and Machine Learning are more useful with college-level math preparation.
Are books enough to start an AI career?+
Books can build important background knowledge, but most AI roles also require hands-on projects, programming practice, portfolio work, and possibly formal study through a bootcamp, certificate, or degree program.
Conclusion & Next Steps
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.
If you want a more structured next step after reading, compare AIFwD’s guides to machine learning bootcamps, AI master’s programs, and online AI certificate programs.
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