I'm so excited that stanford is offering free online version of machine learning course this year, where you get to participate online like a student, finish the assignments(in time) and give the exams.
Linear algebra is one must have prerequisite for ML. I've done a course on same while I was in college. But, at that time, I learned more of calculations in linear algebra(multiplying matrices, calculating row reduced echelon form, calculating eigenvalues etc) and less of concepts. Now, the time has come when I really need to "understand" what those computations mean and what is the significance.
So, I watched some video lectures from MIT OCW Linear algebra course. I definitely recommend these lectures to everyone looking for a solid introduction to the topic.
As always, for better understanding and quick review, I took some notes. They can be found here.
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