awesome-machine-learning/books.md

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.

Deep Learning

• 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

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 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

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