The Math Required for Machine Learning
This article was written by Harsh Sikka. This version is a summary of the original article.
Start with Mathematics for Machine Learning Specialization on Coursera. If starting from complete scratch, the topics you should certainly review/cover, in any order are as follows:
Linear Algebra — Professor Strang’s textbook and MIT Open Courseware course are recommended for good reason. Khan Academy also has some great resources, and there is a helpful set of review notes from Stanford.
Multivariate Calculus — Again, MIT Open Courseware has good courses, and so does Khan Academy.
Probability — Stanford’s CS 229, a course I’ve mentioned later, has an awesome probability review worth checking out.
Once you’ve finished the resources above, I’d say you’re in a great place to tackle the Andrew Ng Coursera Course or its more mature, mathematically rigorous older brother, CS 229.
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