While artificial intelligence is developing rapidly, AI learning resources are also growing at an equally rapid pace. In addition to online tutorials and learning resources, there are many good quality artificial intelligence and machine learning books available online for free. It will help students and professionals to know about artificial intelligence in detail.Many researchers and experts in artificial intelligence have made their books available for public use. This means that people can learn about artificial intelligence concepts like deep learning, reinforcement learning, machine learning systems, etc. without spending money on expensive books.Here are 10 free AI and machine learning books available online in 2026.
Fundamentals of Machine Learning
The focus of this book is to discuss the mathematical concepts behind machine learning models. Concepts discussed in this book include statistical learning theory, algorithms, and predictive modeling. This book is typically used in a graduate program context.Link: http://cs.nyu.edu/~mohri/mlbook/
Understanding Deep Learning
In this book, the focus is on the concepts behind deep learning models and neural networks. It aims to help readers understand complex concepts in simple terms. Some of the concepts discussed in this book include how deep learning models learn patterns in large data sets.Link: http://udlbook.github.io/udlbook/
Algorithms for Machine Learning
These are the algorithms that form the backbone of machine learning models. The focus of this book is to help readers understand the mathematical concepts behind machine learning algorithms.Link: http://algorithmsbook.com
Reinforcement learning
Originally intended for educational use, this book is about reinforcement learning, a branch of AI that involves learning by interacting with the environment, using trial and error.Link: http://andrew.cmu.edu/course/10-703/
Introduction to Machine Learning Systems
With AI systems becoming increasingly sophisticated, building machine learning infrastructure is now a critical skill, and this book is about building machine learning infrastructure, specifically for use in real-world scenarios.Link: http://mlsysbook.ai/book/assets/download.pdf
Deep learning
It is considered to be one of the most influential books in AI, especially regarding learning about neural networks, optimization, and learning systems, especially those used in the generative AI tools available today.Link: http://deeplearningbook.org
Distributive reinforcement learning
This book dives into a specialized branch of reinforcement learning that studies how AI agents model uncertainty and probability distributions when making decisions.Link: http://direct.mit.edu/books/oa-monographs/4968/Distributional-Reinforcement-Learning
Multi-agent reinforcement learning
Multi-agent systems study how multiple AI agents interact, cooperate, or compete with each other. This book explores the theory and algorithms behind such systems.Link: http://marl-book.com
Agents in AI’s Long Game
This book explores the long-term development of intelligent agents and their role in the broader evolution of artificial intelligence.Link: http://direct.mit.edu/books/oa-monographs/5340/Agents-in-the-Long-Game-of-AI
Fairness and Machine Learning
As AI systems impact jobs, finance, and public policy, concerns about bias and fairness have become increasingly important. This book examines ethical challenges and suggests ways to build better machine learning systems.Link: http://fairmlbook.org
A growing open library for AI learners
The availability of these books reflects a broader shift in the AI community. Researchers and universities are increasingly publishing their work as open educational resources, enabling access to cutting-edge knowledge beyond academic institutions.For students, developers, and researchers, these resources offer a rare opportunity: access to university-level AI education without the cost of textbooks.In a field as rapidly evolving as artificial intelligence, such open access can play an important role in shaping the next generation of innovators.
