10 free AI and Machine Learning books you can read online in 2026

While artificial intelligence is advancing at a rapid rate, the learning resources for artificial intelligence are also increasing at an equally rapid rate. Apart from online tutorials and learning resources, there are many good-quality artificial intelligence and machine learning books available online for free. This will help students and professionals learn about artificial intelligence in detail.

Many researchers and artificial intelligence experts have made their books available for public use. This means that people can learn about artificial intelligence concepts such as 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.

Foundations of Machine Learning

The focus of this book is to discuss the mathematical concepts behind machine learning models. Some of the concepts that are discussed in this book include statistical learning theory, algorithms, and predictive modeling. This book is normally used in the context of a graduate program.

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. The aim is to help readers understand complex concepts in simple terms. Some of the concepts that are discussed in this book include how deep learning models learn patterns in large datasets.


Link: http://udlbook.github.io/udlbook/

Algorithms for Machine Learning

It is 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 academic use, this book is about reinforcement learning, which is a branch of AI where learning takes place through interaction 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, especially for use in real-world scenarios.

Link: http://mlsysbook.ai/book/assets/download.pdf

Deep Learning

This is considered one of the most influential books in AI, especially with regard to learning about neural networks, optimisation, and learning systems, especially those used in generative AI tools available today.


Link: http://deeplearningbook.org

Distributional Reinforcement Learning

This book dives into a specialised branch of reinforcement learning that studies how AI agents model uncertainty and probability distributions while 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 the Long Game of AI

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 influence hiring, finance, and public policy, concerns about bias and fairness have become increasingly important. This book examines ethical challenges and proposes methods for building fairer 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, making advanced knowledge accessible beyond academic institutions.

For students, developers, and researchers, these resources provide a rare opportunity: access to university-level AI education without the cost of textbooks.

In a field evolving as quickly as artificial intelligence, such open access could play a crucial role in shaping the next generation of innovators.
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