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
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