A Coursera specialization from Imperial College London that covers the math underlying machine learning, including linear algebra, multivariable calculus, and principal component analysis. It is aimed at refreshing the math needed to follow ML material.

One commenter points to it as a proper refresher on the math behind machine learning, contrasting it with rote hand-calculation learned in college.

What commenters said (1)

1. Recommends__rito__2023in “Generative AI learning path”

   Learn Machine Learning first. Andrew Ng's course is great. Then learn Deep Learning through Andrew NG's course on Coursera.

   Then go through fast.ai for practical projects.

   Learn PyTorch well. There are many books around. I like the one from Manning and the one by Sebastian Raschka.

   Then chart your own path from there.

   Many people "learned Math" in college, but these were actually mostly mindless following of algorithms to solve problems by hand. If you want proper refreshers, go through ICL's Mathematics for Machine Learning Specialization on Coursera or 3blue1brown's series on Linear Algebra and Calculus.

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