# Resources
I've compiled some of my favorite resources on a variety of topics here. They have helped me a ton when trying to learn new topics and I still refer to them constantly. Hopefully you will find them helpful as well.
# Machine Learning
Andrew Ng's Introduction to Machine Learning (opens new window)
This is one of the most popular introductory machine learning courses and for good reason. Andrew is an incredibly knowledgeable instructor and his course is well worth your time.
Machine Learning Cheatsheet (opens new window)
I Liked using this in tandem with Andrew's course, it has great to-the-point descriptions of many of the most important basic concepts in ML
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Another great introductory ML course, with a more top down approach.
# Computer Science
Geeks for Geeks (opens new window)
A great reference site for all things comp sci. My primary go-to when I need to see implementation examples.
Martin Fowler's Site (opens new window)
If you want to learn the tried and true techniques for writing beautiful, bug-free code, this is the site for you.
Introduction to Theoretical Computer Science (opens new window)
As the title implies this is all theory. A very comprehensive guide to the math and principles that all of computational science is based on.
MIT Introduction to MATLAB (opens new window)
If you want to become great at using MATLAB, this is the course for you. It's a perfect supplement if you really want to understand how to implement your ML algorithms.
# Mathematics
MIT Linear Algebra (opens new window)
Incredibly informative course, Gilbert Strang is a wonderful instructor. A strong grasp of linear algebra is essential if you want to understand, analyze, and write your own ML algorithms.
Introduction to Higher Mathematics (opens new window)
Another comprehensive guide if you want to really understand the core tenants of Mathematics. The foundation of computing is built on computational mathematics and logic.