Open Source Machine Learning Degree

Learn machine learning for free.

This website is inspired by the datasciencemasters/go and open-source-cs-degree github pages. This one is specifically for machine learning and features textbooks, textbook-length lecture notes, and similar materials found with a simple google search. This repository is meant as a general guide and resource for a free education.

Note: Please report any broken links as an issue on the Github page. Thanks!

Mathematics

Calculus

• Calculus by Gilbert Strang pdf

Linear Algebra

• Linear Algebra by Jim Hefferon pdf

More Linear Algebra

• Linear Algebra Done Right by Sheldon Axler pdf
• Advanced Linear Algebra by Steven Roman pdf
• Advanced Linear Algebra by Bruce E. Shapiro pdf

Even More Damn Linear Algebra

• A Collection of Notes on Numerical Linear Algebra by Robert A. van de Geijn pdf (optional donation to the author on his website)
• Numerical Linear Algebra by Lloyd N. Trefethen, David Bau, III Google Books

Probability and Statistics

• Introduction to Probability by Charles M. Grinstead and Laurie Snellpdf
• All of Statistics by Larry Wasserman pdf

Introductory Machine Learning

• Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan pdf
• Introduction to Machine Learning - The Wikipedia Guide by Nixonite pdf
• Introduction to Machine Learning by Ethem Alpaydin pdf

Computer Vision

• Computer Vision: Algorithms and Applications by Richard Szeliski pdf

Reinforcement Learning

• Introduction to Reinforcement Learning by Sutton and Barto html

Probabilistic Graphical Models

• A Brief Introduction to Graphical Models and Bayesian Networks by Kevin Murphy pdf html
• An Introduction to Graphical Models by Kevin Murphy pdf
• Probabilistic Graphical Models: Principles and Techniques by Koller, Friedman pdf
• Bayesian Reasoning and Machine Learning by David Barber pdf

Applied Machine Learning

• Natural Language Processing with Python by Steven Bird et al. pdf (Python 2) html (Python 3)
• Machine Learning in Action by Peter Harrington pdf
• An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani pdf

Machine Learning - HARD MODE

• Elements of Statistical Learning by Hastie et al. pdf
• Pattern Recognition and Machine Learning by Christopher M. Bishop pdf
• Information Theory, Inference, and Learning Algorithms by David J. C. MacKay pdf

Legal Stuff

If you're the original author of any of these books, and would like me to remove the links to your material, just send me an email at programminglinguist@gmail.com