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 booklength lecture notes for study.
By Allen Sarkisyan, DataScience, Inc.
Learn machine learning for free, because free is better than notfree.
This website is inspired by the datasciencemasters/go and opensourcecsdegree Github pages. This one is specifically for machine learning and features textbooks, textbooklength 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.
Related:
Top Stories Past 30 Days

