[backpropaganda]

What is your math experience/background? In any case, for the average case, this course is probably the best for those who want a slightly deeper understanding of machine learning than offered by Andrew Ng's courses: https://work.caltech.edu/lectures.html

[davidsrosenberg]

I also really like the Abu-Mostafa course from caltech you link, and their book. If you want to get a taste of generalization bounds and statistical learning theory (e.g. VC dimension), he gives the gentlest introduction I've seen.

[Jasonseah]

can say at a very basic level ... I don't really focus on the study at my early age. I still can understand the equation but not really know why and how to implement it, and come to a very complex situation I can't make sense on the calculation ... Every calculation that Andrew Ng teach are new to me even for the matrix, vector, and sum over are new to me. Even tho Andrew Ng did say "if you don't understand math is ok." but I still love to understand math for my machine learning career, that's why I seek for help.

[backpropaganda]

You need to pick up linear algebra and basic probability.

CS229's linear algebra review is pretty good: http://cs229.stanford.edu/section/cs229-linalg.pdf

For more depth, Trefthen and Bau's Numerical Linear Algebra.

For a video course, Gilbert Strang's MIT OCW course on Linear Algebra.

Review of probability: https://see.stanford.edu/materials/aimlcs229/cs229-prob.pdf