Open Source Machine Learning Degree
A set of free resources for learning machine learning, inspired by similar open source degree resources. Find links to books and book-length lecture notes for study.
By Allen Sarkisyan, DataScience, Inc.
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!
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.
Bio: Allen Sarkisyan is a data analyst at Data Science, Inc. He has a degree in math, is currently working on chess data analysis, and has a cat companion who probably knows more linear algebra than he does at this point, given her propensity to sleep on his textbooks and notes. Allen can be contacted at programminglinguist@gmail.com.
Original. Reposted with permission.
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