The following is a list of free and/or open source books on machine learning, statistics, data mining, etc.
- 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.
- Deep Learning - An MIT Press book
- Deep Learning with Python
- Deep Learning with Python, Second 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 Lanuage Processing Early access book
- 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 Lanuage 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
- A Brief Introduction to Neural Networks
- Neural Networks and Deep Learning
- Graph Neural Networks in Action
- 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