#Open Source Machine Learning Degree
Learn machine learning for free, because free is better than not-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!
Calculus
- Calculus by Gilbert Strang pdf
Proofs
- How to Prove It by Daniel J. Velleman 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
- 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: Algorithms and Applications by Richard Szeliski pdf
- Introduction to Reinforcement Learning by Sutton and Barto html
- 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
- 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
- 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
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 [email protected]