The following is a list of free, open source books on machine learning, statistics, data-mining, etc.
- The Hundred-Page Machine Learning Book
- Real World Machine Learning [Free Chapters]
- An Introduction To Statistical Learning - Book + R 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 access book
- Machine Learning Systems Early access book
- 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 as introduction on how to use R.
- 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
- Deep Learning - An MIT Press book
- Deep Learning with Python
- 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
- Coursera Course Book on NLP
- NLTK
- Foundations of Statistical Natural Language Processing
- Natural Language Processing in Action Early access book
- 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