Machine Learning Resources

Last updated date : 14/2/2016
The following are the pre-requisites for learning machine learning. I have collected these resources from various quora answers, books, and MOOCs. 

GitHub Repo

https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/README.md
https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md
https://github.com/owainlewis/awesome-artificial-intelligence
https://github.com/prakhar1989/awesome-courses#machine-learning

Mathematics :

MIT : http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2005/

http://datascience.ibm.com/blog/the-mathematics-of-machine-learning/

Linear Algebra:

• Khan Academy - Linear Algebra
• MIT - Linear Algebra
• Linear Algebra, Theory, and Applications. Kuttler
• Linear Algebra Done Wrong. Treil
• Convex Optimization. Boyd and Vandenberghe
• Applied Numerical Linear Algebra. James Demmel
• Applied Numerical Computing. Vandenberghe
• https://class.coursera.org/matrix-002/lecture

Calculus:

• MIT - Variable Calculus
• Khan Academy - Differential Calculus
• Khan Academy - Integral Calculus
• Khan Academy - Multivariable Calculus
• Khan Academy - Differential Equations

Statistics and Probability:

• Inferential Statistics
• Descriptive Statistics
• http://www.wzchen.com/probability-cheatsheet
• https://www.youtube.com/playlist?list=PLLVplP8OIVc8EktkrD3Q8td0GmId7DjW0 (stat 110 , harvard )

Programming:

Others:

• Welch Labs

Machine Learning Books:

• Elements of Statistical Learning. Hastie, Tibshirani, Friedman
• All of Statistics. Larry Wasserman
• Machine Learning and Bayesian Reasoning. David Barber
• Gaussian Processes for Machine Learning. Rasmussen and Williams
• Information Theory, Inference, and Learning Algorithms. David MacKay
• Introduction to Machine Learning. Smola and Vishwanathan
• A Probabilistic Theory of Pattern Recognition. Devroye, Gyorfi, Lugosi.
• Introduction to Information Retrieval. Manning, Rhagavan, Shutze
• Forecasting: principles and practice. Hyndman, Athanasopoulos. (Online Book)

Probability Books:

• Introduction to statistical thought. Lavine
• Basic Probability Theory. Robert Ash
• Introduction to probability. Grinstead and Snell
• Principle of Uncertainty. Kadane

Books I purchased:

• Cracking the Coding Interview 
• Graph Theory
• Artificial Intelligence
• Machine Learning
• Learning with python
• Dive into Python

CMU ML course : http://alex.smola.org/teaching/cmu2013-10-701x/
Tom Mitchell : http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml
Mathematical monk's ML : https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA
Stanford's ML notes : http://cs229.stanford.edu/materials.html
https://www.youtube.com/playlist?list=PLD63A284B7615313A
http://cs231n.github.io/


ML Projects : https://docs.google.com/document/d/1Ph-__LSg6I-BftTY3yBk2erh8hp0Kik12fjj9PUOygM/pub

How to do AI research : http://net.pku.edu.cn/~cuibin/resources/MIT-do-research.pdf