The following is a list of free and/or open source books on machine learning, statistics, data mining, etc.

Machine Learning / Data Mining

Distributed Machine Learning Patterns - Book (free to read online) + Code
The Hundred-Page Machine Learning Book
Real World Machine Learning [Free Chapters]
An Introduction To Statistical Learning With Applications In R - Book + R Code
An Introduction To Statistical Learning With Applications In Python - Book + Python Code
Elements of Statistical Learning - Book
Computer Age Statistical Inference (CASI) (Permalink as of October 2017) - Book
Probabilistic Programming & Bayesian Methods for Hackers - Book + IPython Notebooks
Think Bayes - Book + Python Code
Information Theory, Inference, and Learning Algorithms
Gaussian Processes for Machine Learning
Data Intensive Text Processing w/ MapReduce
Reinforcement Learning: - An Introduction (Permalink to Nov 2017 Draft)
Mining Massive Datasets
A First Encounter with Machine Learning
Pattern Recognition and Machine Learning
Machine Learning & Bayesian Reasoning
Introduction to Machine Learning - Alex Smola and S.V.N. Vishwanathan
A Probabilistic Theory of Pattern Recognition
Introduction to Information Retrieval
Forecasting: principles and practice
Practical Artificial Intelligence Programming in Java
Introduction to Machine Learning - Amnon Shashua
Reinforcement Learning
Machine Learning
A Quest for AI
Introduction to Applied Bayesian Statistics and Estimation for Social Scientists - Scott M. Lynch
Bayesian Modeling, Inference and Prediction
A Course in Machine Learning
Machine Learning, Neural and Statistical Classification
Bayesian Reasoning and Machine Learning Book+MatlabToolBox
R Programming for Data Science
Data Mining - Practical Machine Learning Tools and Techniques Book
Machine Learning with TensorFlow Early book access
Machine Learning Systems Early book access
Hands‑On Machine Learning with Scikit‑Learn and TensorFlow - Aurélien Géron
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data - Wickham and Grolemund. Great introduction on how to use R language.
Advanced R - Hadley Wickham. More advanced usage of R for programming.
Graph-Powered Machine Learning - Alessandro Negro. Combining graph theory and models to improve machine learning projects.
Machine Learning for Dummies
Machine Learning for Mortals (Mere and Otherwise) - Early access book that provides basics of machine learning and using R programming language.
Grokking Machine Learning - Early access book that introduces the most valuable machine learning techniques.

Foundations of Machine Learning - Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
Understanding Machine Learning - Shai Shalev-Shwartz and Shai Ben-David
Fighting Churn With Data [Free Chapter] Carl Gold - Hands on course in applied data science in Python and SQL, taught through the use case of customer churn.
Machine Learning Bookcamp - Alexey Grigorev - a project-based approach on learning machine learning (early access).
AI Summer A blog to help you learn Deep Learning an Artificial Intelligence
Mathematics for Machine Learning
Approaching Almost any Machine learning problem Abhishek Thakur
MLOps Engineering at Scale - Carl Osipov - Guide to bringing your experimental machine learning code to production using serverless capabilities from major cloud providers.
AI-Powered Search - Trey Grainger, Doug Turnbull, Max Irwin - Early access book that teaches you how to build search engines that automatically understand the intention of a query in order to deliver significantly better results.
Ensemble Methods for Machine Learning - Gautam Kunapuli - Early access book that teaches you to implement the most important ensemble machine learning methods from scratch.
Machine Learning Engineering in Action - Ben Wilson - Field-tested tips, tricks, and design patterns for building Machine Learning projects that are deployable, maintainable, and secure from concept to production.
Privacy-Preserving Machine Learning - J. Morris Chang, Di Zhuang, G. Dumindu Samaraweera - Keep sensitive user data safe and secure, without sacrificing the accuracy of your machine learning models.
Automated Machine Learning in Action - Qingquan Song, Haifeng Jin, and Xia Hu - Optimize every stage of your machine learning pipelines with powerful automation components and cutting-edge tools like AutoKeras and Keras Tuner.
Distributed Machine Learning Patterns - Yuan Tang - Practical patterns for scaling machine learning from your laptop to a distributed cluster.
Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI - Robert (Munro) Monarch - a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process.
Feature Engineering Bookcamp - Maurucio Aniche - This book’s practical case-studies reveal feature engineering techniques that upgrade your data wrangling—and your ML results.
Metalearning: Applications to Automated Machine Learning and Data Mining - Pavel Brazdil, Jan N. van Rijn, Carlos Soares, Joaquin Vanschoren
Managing Machine Learning Projects: From design to deployment - Simon Thompson
Causal AI - Robert Ness - Practical introduction to building AI models that can reason about causality.
Bayesian Optimization in Action - Quan Nguyen - Book about building Bayesian optimization systems from the ground up.
Machine Learning Algorithms in Depth - Vadim Smolyakov - Book about practical implementations of dozens of ML algorithms.
Optimization Algorithms - Alaa Khamis - Book about how to solve design, planning, and control problems using modern machine learning and AI techniques.
Practical Gradient Boosting by Guillaume Saupin
Machine Learning System Design - Valerii Babushkin and Arseny Kravchenko - A book about planning and designing successful ML applications.
Fight Fraud with Machine Learning - by Ashish Ranjan Jha - A book about developing scalable and tunable models that can spot and stop fraudulent activity.
Machine Learning for Drug Discovery - by Noah Flynn - A book that introduces the machine learning and deep learning techniques that drive modern medical research.
Probabilistic Machine Learning - 2022 edition By Kevin P. Murphy - A must have for PhD students and ML researchers. An exceptional book that covers the basic foundational ML concepts like optimization decision theory information theory and ML maths(linear algebra & probability theory) before delving into traditional but important models Linear Models , then modern supervised and unsupervised Deep learning models.
Probabilistic Machine Learning: Advanced Topics - 2023 edition By Kevin P. Murphy Sequel to the first book for those seeking to learn about more niche but important topics. Also includes technical coverage on latest models like diffusion and generative modeling.
Python Feature Engineering Cookbook - A hands-on guide to streamline data preprocessing and feature engineering in your machine learning projects.

Deep Learning

Deep Learning - An MIT Press book
Deep Learning with Python
Deep Learning with Python, Second Edition Early access book
Deep Learning with Python, Third Edition Early access book
Deep Learning with JavaScript Early access book
Grokking Deep Learning Early access book
Deep Learning for Search Early access book
Deep Learning and the Game of Go Early access book
Machine Learning for Business Early access book
Probabilistic Deep Learning with Python Early access book
Deep Learning with Structured Data Early access book
Deep Learning[Ian Goodfellow, Yoshua Bengio and Aaron Courville]
Deep Learning with Python, Second Edition
Inside Deep Learning Early access book
Math and Architectures of Deep Learning Early access book
Deep Learning for Natural Language Processing Early access book
Deep Learning with R, Third Edition
AI Model Evaluation

Natural Language Processing

Coursera Course Book on NLP
NLTK
Foundations of Statistical Natural Language Processing
Natural Language Processing in Action Early access book
Natural Language Processing in Action, Second Edition Early access book
Real-World Natural Language Processing Early access book
Essential Natural Language Processing Early access book
Deep Learning for Natural Language Processing Early access book
Natural Language Processing in Action, Second Edition Early access book
Getting Started with Natural Language Processing in Action Early access book
Transfer Learnin for Natural Language Processing by Paul Azunre

Information Retrieval

An Introduction to Information Retrieval

Neural Networks

A Brief Introduction to Neural Networks
Neural Networks and Deep Learning
Graph Neural Networks in Action

Probability & Statistics

Think Stats - Book + Python Code
From Algorithms to Z-Scores - Book
The Art of R Programming - Book (Not Finished)
Introduction to statistical thought
Basic Probability Theory
Introduction to probability - By Dartmouth College
Probability & Statistics Cookbook
Introduction to Probability - Book and course by MIT
The Elements of Statistical Learning: Data Mining, Inference, and Prediction. - Book
An Introduction to Statistical Learning with Applications in R - Book
Introduction to Probability and Statistics Using R - Book
Advanced R Programming - Book
Practical Regression and Anova using R - Book
R practicals - Book
The R Inferno - Book
Probability Theory: The Logic of Science - By Jaynes

Linear Algebra

The Matrix Cookbook
Linear Algebra by Shilov
Linear Algebra Done Wrong
Linear Algebra, Theory, and Applications
Convex Optimization
Applied Numerical Computing

Calculus

Calculus Made Easy
calculus by ron larson
Active Calculus by Matt Boelkins

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