[andyxor]

there is not much math, don't let the academic papers razzle dazzle you, they make things look complicated to get their grants

in practice, as long as you've studied basic calculus and understand how to find a minimum of a function via derivative you're good, there is your "gradient descent" in a nutshell: https://www.mathsisfun.com/calculus/maxima-minima.html

everything else is plug-and-play from existing libraries

you can ask any "data scientist" or "ML engineer" what they do all day, it's a whole lot of copy paste, and tweaking the data and parameters through trial and error until it fits

Edit: Ok , it would also help to understand dimensionality reduction via PCA/SVD at least once, it's available in any linear algebra book: https://en.wikipedia.org/wiki/Singular_value_decomposition , https://en.wikipedia.org/wiki/Principal_component_analysis that's probably the best and most "scientific" part of ML

[IWantToRelocate]

Thank you! appreciate your reply, honestly. i have an engineering degree but it has nothing to do with computers, so i kinda struggle sometimes. I basically want to get fluent with the metrics/performance when applying ML/NN, when to tweak what, how to improve some stuff, what algorithm works best for some of the problems, etc.

[aborsy]

This, in a nutshell.