Top 70 Must-Read Books on Artificial Intelligence for 2024

Artificial Intelligence (AI) is transforming the world at a rapid pace, impacting various industries from healthcare to finance. Staying updated with the latest developments and understanding the core concepts is crucial for professionals and enthusiasts alike.

This list of the top must-read books on artificial intelligence for 2024 offers a comprehensive guide to both beginners and experts. Let’s begin the journey to ultimate knowledge!

The Top Must-Read Books on Artificial Intelligence

These books cover a range of topics from fundamental principles to advanced applications, providing valuable insights and knowledge to help you stay ahead in the field of AI.

Introduction to Artificial Intelligence

Are you an AI beginner? Here are a few books you can set your base on AI:

1. Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig

This book is often considered the bible of AI. It covers a wide array of topics, providing a thorough introduction to the field. The authors delve into foundational concepts, algorithms, and the latest advancements, making it a must-read for anyone serious about understanding AI.

2. Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark

Max Tegmark explores the implications of AI on society, ethics, and the future of humanity. This book is accessible to both beginners and those with some knowledge of AI, offering thought-provoking insights into how AI could shape our future.

3. Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

Nick Bostrom examines the potential risks and challenges of developing superintelligent AI. This book is crucial for understanding the ethical and existential questions surrounding AI development. It is a profound exploration of the future scenarios of AI’s impact on humanity.

4. Artificial Intelligence: Foundations of Computational Agents by David L. Poole and Alan K. Mackworth

This book provides a solid foundation in AI, emphasizing the importance of computational agents. It is designed for those new to AI and covers basic principles, methodologies, and applications. The structured approach makes complex concepts accessible.

5. AI: A Very Short Introduction by Margaret A. Boden

Margaret Boden offers a concise and clear overview of AI, perfect for readers who want a quick yet comprehensive introduction to the field. The book covers the history, current state, and future of AI, making it a great starting point for beginners.

6. Python Machine Learning by Sebastian Raschka and Vahid Mirjalil

This book is an excellent introduction to machine learning with Python. It covers the basics of machine learning algorithms and provides hands-on examples to help beginners understand how to implement these algorithms using Python. The clear explanations and practical approach make it a great starting point for anyone new to AI.

7. Artificial Intelligence: A New Synthesis by Nils J. Nilsson

Nils J. Nilsson offers a comprehensive introduction to AI, combining theoretical foundations with practical applications. The book covers essential topics such as search algorithms, knowledge representation, and machine learning, providing a well-rounded introduction for beginners.

8. Machine Learning for Dummies by John Paul Mueller and Luca Massaron

This book breaks down complex machine-learning concepts into easy-to-understand language. It covers the basics of AI and machine learning, including how to build models and use algorithms. The approachable style makes it perfect for readers with no prior experience in AI.

9. Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning by James V. Stone

James V. Stone provides a tutorial introduction to the mathematical foundations of deep learning. The book is designed for beginners and explains complex concepts clearly and straightforwardly. It’s ideal for readers who want to understand the mathematics behind AI.

10. AI for People and Business by Alex Castrounis

This book focuses on how AI can be applied in business settings. It explains AI concepts in a non-technical way, making it accessible for business professionals who want to leverage AI to improve their operations and decision-making processes. The practical insights and case studies are valuable for beginners looking to apply AI in real-world scenarios.

11. Machine Learning Yearning by Andrew Ng

Written by AI expert Andrew Ng, this book offers practical advice on how to structure machine learning projects. It’s designed for those who are new to AI and machine learning, providing step-by-step guidance on best practices and common pitfalls. The book is a must-read for anyone starting a career in AI.

12. Artificial Intelligence Basics: A Non-Technical Introduction by Tom Taulli

Tom Taulli provides a non-technical introduction to AI, making it easy for beginners to grasp the fundamental concepts. The book covers a wide range of topics, including machine learning, neural networks, and AI applications in various industries. It’s an excellent resource for those who want a broad overview of AI without getting into technical details.

AI for Intermediate Learners

Now is the time for advanced users. This list is for those having some good basic knowledge of AI:

13. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

This book is a comprehensive guide to deep learning, written by three of the field’s leading experts. It covers a wide range of topics, from basic concepts to advanced techniques, making it perfect for intermediate learners who want to deepen their understanding of neural networks and their applications.

14. Pattern Recognition and Machine Learning by Christopher M. Bishop

Christopher Bishop’s book provides a detailed introduction to pattern recognition and machine learning. The book is ideal for intermediate learners, offering in-depth explanations of algorithms, statistical techniques, and practical applications. It bridges the gap between theory and practice, making it a valuable resource for those looking to advance their knowledge.

15. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron

Aurélien Géron’s hands-on approach makes this book an excellent resource for intermediate learners. It covers the latest tools and techniques in machine learning and deep learning, providing practical examples and projects to help readers apply what they’ve learned. The book is particularly useful for those looking to gain practical experience with popular machine-learning libraries.

16. Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

This book offers a thorough introduction to statistical learning methods, with a focus on applications in data science. It is suitable for intermediate learners who have a basic understanding of statistics and machine learning. The book includes practical examples and exercises, making it a great resource for those looking to enhance their skills.

17. Neural Networks and Deep Learning by Charu C. Aggarwal

Charu Aggarwal’s book provides a comprehensive introduction to neural networks and deep learning, focusing on both the theoretical foundations and practical implementations. It is ideal for intermediate learners who want to explore the depths of neural network architectures and their applications in various domains.

18. Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto

This book is a classic in the field of reinforcement learning. It covers the fundamental concepts and algorithms of reinforcement learning, with detailed explanations and practical examples. The book is perfect for intermediate learners who want to understand how agents can learn to make decisions through trial and error.

19. Applied Artificial Intelligence: A Handbook for Business Leaders by Mariya Yao, Adelyn Zhou, and Marlene Jia

This book bridges the gap between AI theory and business applications. It provides practical insights into how AI can be implemented in various industries, making it an excellent resource for intermediate learners who want to apply their AI knowledge in real-world business scenarios.

20. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy

Kevin Murphy’s book offers a comprehensive overview of machine learning from a probabilistic viewpoint. It covers a wide range of topics, including Bayesian networks, graphical models, and inference algorithms. The book is suitable for intermediate learners who have a solid foundation in probability and statistics.

Advanced AI Concepts

Wish to dig further? Here are more AI books for more advanced readers:

21. Artificial Intelligence: Structures and Strategies for Complex Problem Solving by George F. Luger

This book delves into advanced AI techniques and strategies for solving complex problems. It covers a range of topics, including heuristic search, knowledge representation, and expert systems. It is suitable for readers with a solid foundation in AI looking to explore more sophisticated concepts and methodologies.

22. Probabilistic Graphical Models: Principles and Techniques by Daphne Koller and Nir Friedman

Daphne Koller and Nir Friedman provide an in-depth exploration of probabilistic graphical models (PGMs). This book covers both the theoretical foundations and practical applications of PGMs, making it essential for advanced learners who want to understand the intersection of probability theory and AI.

23. Bayesian Reasoning and Machine Learning by David Barber

David Barber’s book focuses on the principles of Bayesian reasoning and its applications in machine learning. It is designed for advanced readers who want to dive deep into probabilistic models, Bayesian networks, and inference techniques. The book includes practical examples and exercises to enhance understanding.

24. Deep Reinforcement Learning Hands-On by Maxim Lapan

This book provides a hands-on approach to deep reinforcement learning, combining theory with practical implementation. It covers advanced topics such as policy gradient methods, deep Q-networks, and actor-critic algorithms. The practical projects and code examples make it a valuable resource for advanced learners.

25. Advanced Machine Learning with Python by John Hearty

John Hearty’s book explores advanced machine-learning techniques using Python. It covers topics such as ensemble methods, feature engineering, and model optimization. The book is ideal for readers who have mastered the basics and are looking to apply advanced techniques in real-world scenarios.

26. Deep Learning for Computer Vision by Rajalingappaa Shanmugamani

This book focuses on applying deep learning techniques to computer vision tasks. It covers convolutional neural networks, image recognition, and object detection. It is designed for advanced learners who want to specialize in computer vision and gain practical experience with state-of-the-art techniques.

27. The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

This book is a comprehensive guide to statistical learning, covering a wide range of advanced topics such as kernel methods, boosting, and support vector machines. It is suitable for readers with a strong background in statistics and machine learning who want to explore advanced methods and algorithms.

28. Reinforcement Learning and Optimal Control by Dimitri P. Bertsekas

Dimitri Bertsekas provides an in-depth exploration of reinforcement learning and optimal control theory. The book covers advanced topics such as dynamic programming, Monte Carlo methods, and temporal difference learning. It is perfect for advanced learners who want to understand the mathematical foundations of reinforcement learning.

29. Natural Language Processing with PyTorch by Delip Rao and Brian McMahan

This book offers an advanced guide to natural language processing (NLP) using PyTorch. It covers topics such as word embeddings, sequence models, and transformers. The hands-on approach and practical examples make it ideal for advanced learners interested in NLP.

30. AI: A Systems Approach by M. Tim Jones

M. Tim Jones provides a comprehensive overview of AI systems, focusing on the integration of various AI components. The book covers advanced topics such as intelligent agents, multi-agent systems, and AI architectures. It is designed for advanced learners who want to understand how different AI techniques can be combined to build complex systems.

AI and Machine Learning

Wish to read more on AI? We have some more books on AI and machine learning:

31. Machine Learning Yearning by Andrew Ng

Andrew Ng’s book provides practical advice on structuring machine learning projects. It covers best practices, common pitfalls, and step-by-step guidance for implementing machine learning systems. This book is ideal for those looking to apply machine-learning techniques effectively in real-world projects.

32. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

This comprehensive guide to deep learning is essential for understanding advanced machine learning techniques. It covers a wide range of topics, including neural networks, optimization algorithms, and various deep learning architectures. The book is suitable for readers with a foundational understanding of machine learning.

33. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron

Aurélien Géron’s book offers a practical approach to machine learning and deep learning using popular libraries like Scikit-Learn, Keras, and TensorFlow. It includes numerous examples and projects that help readers apply machine learning algorithms to real-world problems, making it perfect for hands-on learners.

34. Pattern Recognition and Machine Learning by Christopher M. Bishop

Christopher Bishop’s book is an in-depth exploration of pattern recognition and machine learning. It covers both theoretical and practical aspects, providing a solid foundation in statistical techniques and their applications in machine learning. This book is suitable for intermediate to advanced learners.

35. The Hundred-Page Machine Learning Book by Andriy Burkov

Andriy Burkov provides a concise yet comprehensive introduction to machine learning in just 100 pages. The book covers essential algorithms, techniques, and best practices, making it a great quick reference for both beginners and experienced practitioners looking to refresh their knowledge.

36. Introduction to Machine Learning with Python by Andreas C. Müller and Sarah Guido

This book introduces machine learning concepts using Python, with a focus on practical implementation. It covers the basics of machine learning, data processing, model evaluation, and more. It’s an excellent resource for beginners who want to learn how to implement machine-learning algorithms in Python.

37. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy

Kevin Murphy’s book provides a comprehensive overview of machine learning from a probabilistic standpoint. It covers a wide range of topics, including Bayesian networks, graphical models, and inference algorithms. The book is ideal for readers with a strong foundation in probability and statistics.

38. Deep Learning for Coders with Fastai and PyTorch by Jeremy Howard and Sylvain Gugger

This book teaches deep learning using the Fastai library and PyTorch. It’s designed for coders who want to dive into deep learning, with practical tutorials and projects that make complex concepts accessible. The hands-on approach is perfect for learners who want to gain experience by building real-world applications.

39. Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Melanie Mitchell offers a broad overview of AI and its impact on society. The book covers key concepts in machine learning and AI, while also addressing ethical and societal implications. It’s suitable for readers who want to understand AI from both a technical and philosophical perspective.

40. Practical Statistics for Data Scientists by Peter Bruce and Andrew Bruce

This book bridges the gap between statistical theory and practical application in data science. It covers essential statistical techniques used in machine learning, providing clear explanations and practical examples. It’s ideal for data scientists who want to enhance their statistical knowledge and apply it to machine-learning problems.

AI in Practice

Now that you have learned all about AI, it’s time to learn how to use in practically:

41. Applied Artificial Intelligence: A Handbook for Business Leaders by Mariya Yao, Adelyn Zhou, and Marlene Jia

This book focuses on how to implement AI solutions in various business contexts. It provides practical strategies, real-world case studies, and step-by-step guidance for business leaders who want to harness the power of AI to drive innovation and efficiency in their organizations.

42. Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett

This book bridges the gap between data science and business applications. It covers key concepts in data mining and machine learning, with a focus on how these techniques can be applied to solve business problems. The practical approach makes it a valuable resource for professionals looking to leverage data science in their work.

43. Building Machine Learning Powered Applications: Going from Idea to Product by Emmanuel Ameisen

Emmanuel Ameisen provides a practical guide to developing machine learning applications from scratch. The book covers the entire lifecycle of a machine learning project, from ideation and data collection to model deployment and maintenance. It is ideal for practitioners who want to turn AI ideas into functional products.

44. Designing Machine Learning Systems with Python by David Julian

This book offers a practical approach to designing and building machine learning systems using Python. It covers the architectural considerations, tools, and techniques necessary to develop scalable and robust machine learning solutions. The hands-on examples and case studies make it a valuable resource for practitioners.

45. AI for Marketing and Product Innovation: Powerful New Tools for Predicting Trends, Connecting with Customers, and Closing Sales by A. K. Pradeep, Andrew Appel, and Stan Sthanunathan

This book explores the applications of AI in marketing and product development. It provides insights into how AI can be used to predict consumer behavior, personalize marketing efforts, and drive product innovation. The practical examples and case studies demonstrate the real-world impact of AI in the business domain.

46. Real-World Machine Learning by Henrik Brink, Joseph Richards, and Mark Fetherolf

Henrik Brink and his co-authors offer a practical guide to implementing machine learning solutions in real-world scenarios. The book covers a range of topics, including data preprocessing, model selection, and performance evaluation, with a focus on practical applications and case studies.

47. Practical Deep Learning for Cloud, Mobile, and Edge by Anirudh Koul, Siddha Ganju, and Meher Kasam

This book provides a hands-on approach to deploying deep learning models in various environments, including cloud, mobile, and edge devices. It covers practical techniques and tools for building and deploying AI solutions that can run efficiently on different platforms.

48. Machine Learning in Action by Peter Harrington

Peter Harrington’s book is a practical introduction to machine learning, focusing on real-world applications. It covers essential algorithms and techniques, providing hands-on examples and code implementations. The book is ideal for practitioners who want to apply machine learning to solve real problems.

49. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney

This book focuses on data analysis and manipulation using Python. It covers key libraries such as Pandas and NumPy, providing practical examples and techniques for data wrangling, visualization, and analysis. The hands-on approach makes it a valuable resource for data scientists and AI practitioners.

50. Agile Data Science 2.0: Building Data Analytics Applications with Spark by Russell Jurney

Russell Jurney’s book provides a practical guide to building data analytics applications using Agile methodologies and Spark. It covers the entire data pipeline, from data collection and processing to model development and deployment. The book is ideal for practitioners who want to build scalable AI solutions in an Agile environment.

Ethical and Societal Implications of AI

Ever wondered if AI has something to do with ethics? Well it does and we have a list of well-written books on the subject:

51. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil

Cathy O’Neil explores the dark side of big data and algorithms, highlighting how they can perpetuate inequality and undermine democracy. The book discusses real-world examples where AI and data analytics have had harmful impacts, making it a critical read for understanding the ethical challenges posed by AI.

52. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power by Shoshana Zuboff

Shoshana Zuboff’s book delves into how companies use AI and big data to surveil and manipulate individuals for profit. It examines the implications of this surveillance on privacy, autonomy, and democracy, offering a thought-provoking analysis of the power dynamics in the digital age.

53. Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell

Stuart Russell addresses the control problem in AI, questioning how we can ensure that advanced AI systems act in ways that are beneficial to humanity. The book discusses the potential risks of AI and proposes solutions to align AI goals with human values, making it essential for those concerned with the future of AI ethics.

54. AI Ethics by Mark Coeckelbergh

Mark Coeckelbergh provides a comprehensive introduction to AI ethics, covering topics such as bias, accountability, and transparency. The book offers a balanced discussion of the ethical dilemmas and societal impacts of AI, making it a valuable resource for anyone looking to understand the moral considerations of AI development and deployment.

55. The Ethical Algorithm: The Science of Socially Aware Algorithm Design by Michael Kearns and Aaron Roth

This book explores how to design algorithms that are socially aware and ethically sound. Kearns and Roth discuss the challenges of fairness, privacy, and transparency in algorithmic design, offering practical solutions for building ethical AI systems. It is an important read for developers and policymakers alike.

56. Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark

Max Tegmark’s book not only introduces AI concepts but also delves into the broader ethical and societal implications. He explores scenarios for the future of AI, discussing how it can be both a powerful tool and a potential threat. The book encourages readers to think critically about the role of AI in shaping our future.

57. Ethics of Artificial Intelligence and Robotics by Vincent C. Müller (Editor)

This edited volume brings together contributions from leading experts in the field of AI ethics. It covers a wide range of ethical issues, including machine autonomy, moral agency, and the societal impacts of AI and robotics. The diverse perspectives make it a comprehensive resource for understanding the ethical landscape of AI.

58. Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

Meredith Broussard critiques the overreliance on AI and algorithms, arguing that computers often misunderstand the complexities of the real world. The book highlights the limitations of AI and the importance of human judgment, making it a crucial read for understanding the ethical boundaries of AI technology.

59. Rebooting AI: Building Artificial Intelligence We Can Trust by Gary Marcus and Ernest Davis

Gary Marcus and Ernest Davis discuss the current shortcomings of AI and propose ways to build more reliable and trustworthy AI systems. They emphasize the need for transparency, accountability, and rigorous testing in AI development, addressing key ethical concerns and offering practical solutions.

60. Race After Technology: Abolitionist Tools for the New Jim Code by Ruha Benjamin

Ruha Benjamin examines how technology, including AI, can perpetuate racial biases and inequalities. The book explores the concept of the “New Jim Code,” where discriminatory practices are embedded in algorithms and data systems. It offers a critical perspective on the intersection of race, technology, and ethics.

AI and the Future

Last but not least; this category is all about the future of AI. You must read these books on AI and its future if you really wish to master AI:

61. AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee

Kai-Fu Lee explores the competitive dynamics between the US and China in the AI race. He discusses the implications of AI advancements on global economic and political power, offering insights into how AI will shape the future of international relations and economic structures.

62. The Fourth Industrial Revolution by Klaus Schwab

Klaus Schwab examines how emerging technologies, including AI, are driving the fourth industrial revolution. The book explores the transformative impact of AI on industries, economies, and societies, emphasizing the need for collaboration and ethical considerations as we navigate this technological shift.

63. Artificial Intelligence: The Insights You Need from Harvard Business Review

This collection of articles from Harvard Business Review provides a comprehensive overview of the future of AI. It includes insights from leading experts on the potential applications, benefits, and challenges of AI, making it a valuable resource for understanding how AI will influence various sectors and aspects of life.

64. Future Politics: Living Together in a World Transformed by Tech by Jamie Susskind

Jamie Susskind explores the political and societal implications of AI and other technologies. He discusses how AI will reshape governance, democracy, and individual rights, providing a thought-provoking analysis of the future political landscape influenced by technological advancements.

65. Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, and Avi Goldfarb

This book explores how AI transforms decision-making processes by making predictions more accurate and cost-effective. The authors discuss the economic implications of AI and how businesses and societies can adapt to these changes, offering a pragmatic view of AI’s future impact.

66. The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity by Amy Webb

Amy Webb examines the influence of nine major tech companies on the future of AI. She discusses potential scenarios where AI could lead to unintended consequences, emphasizing the need for strategic planning and ethical considerations to steer AI development toward beneficial outcomes for humanity.

67. Our Final Invention: Artificial Intelligence and the End of the Human Era by James Barrat

James Barrat raises concerns about the existential risks posed by advanced AI. He explores potential scenarios where AI could surpass human intelligence, leading to unpredictable and potentially catastrophic outcomes. The book serves as a cautionary tale about the future of AI development.

68. The Singularity Is Near: When Humans Transcend Biology by Ray Kurzweil

Ray Kurzweil predicts the convergence of human and machine intelligence shortly. He discusses the implications of achieving singularity, where AI surpasses human capabilities, and explores how this transformation could impact society, economy, and human identity.

69. Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty and H. James Wilson

This book explores how AI is redefining work and the workforce. The authors discuss the collaboration between humans and machines, highlighting how AI can augment human capabilities and create new opportunities for innovation and productivity in the future workplace.

70. Architects of Intelligence: The Truth About AI from the People Building It by Martin Ford

Martin Ford interviews some of the leading minds in AI to gather insights about the future of the technology. The book covers a wide range of perspectives on AI’s potential, challenges, and ethical considerations, providing a comprehensive view of what the future may hold according to the experts shaping it.