To get the intuition behind the Machine Learning algorithms, we need to have some background in Math, especially Linear Algebra, Probability & Calculus. Consolidating a few cheat-sheets here. A thread 👇 
For Linear Algebra: Topics include Vector spaces, Matrix vector operations, Rank of a matrix, Norms, Eigenvectors and values and a bit of Matrix calculus too.
souravsengupta.com/cds2016/lectur…
(Advanced) cs229.stanford.edu/section/cs229-…
souravsengupta.com/cds2016/lectur…
(Advanced) cs229.stanford.edu/section/cs229-…
For Probability & Statistics: Random variables, expectation, Probability distributions and so on.
stanford.edu/~shervine/teac…
stanford.edu/~shervine/teac…
(Advanced) cs229.stanford.edu/section/cs229-…
stanford.edu/~shervine/teac…
stanford.edu/~shervine/teac…
(Advanced) cs229.stanford.edu/section/cs229-…
For Calculus: Limits, Derivatives, Implicit differentiation, Finding extrema, MVT, Newton's method and Integral calc stuff. The advanced materials are about Matrix calculus - Gradients, Directional derivatives etc.
math.colorado.edu/math2300/resou…
(Advanced) ccrma.stanford.edu/~dattorro/matr…
math.colorado.edu/math2300/resou…
(Advanced) ccrma.stanford.edu/~dattorro/matr…
Bonus: When you are done with all of the above resources and are looking for more advanced stuff, this Math for Machine Learning book (Part 1) is a must-read. And it is absolutely free!
mml-book.github.io
mml-book.github.io
Bonus 2: This is a really nice reference book to Matrix calculus, has things like what is the gradient of an matrix's inverse or its determinant and so on.
math.uwaterloo.ca/~hwolkowi/matr…
math.uwaterloo.ca/~hwolkowi/matr…
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