免费的机器学习和数据挖掘书籍资源合集

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
• Python Data Science Handbook- Oriely
• Mathematics for Machine Learning
• Approaching Almost any Machine learning problem Abhishek Thakur
• AI-Powered Search

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
• Computer Vision: Algorithms and Applications
• Deep Learning[Ian Goodfellow, Yoshua Bengio and Aaron Courville]

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

• An Introduction to Information Retrieval

Neural Networks

• A Brief Introduction to Neural Networks
• Neural Networks and Deep Learning

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