# 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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