Machine Learning / Data Mining
- 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
- 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
- How Machine Learning Works – Mostafa Samir. Early access book that intorduces machine learning from both practical and theoretical aspects in a non-threating way.
- 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
Deep Learning
- 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
- Probabilistic Deep Learning with Python Early access book
- Deep Learning with Structured Data Early access book
Natural Language Processing
- Coursera Course Book on NLP
- NLTK
- Foundations of Statistical Natural Language Processing
- Natural Language Processing in Action Early access book
- Real-World Natural Language Processing Early access book
- Essential Natural Language Processing Early access book
Information Retrieval
Neural Networks
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
- Linear Algebra and its applications by Gilbert strang
- The Matrix Cookbook
- Linear Algebra by Shilov
- Linear Algebra Done Wrong
- Linear Algebra, Theory, and Applications
- Convex Optimization
- Applied Numerical Computing