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 s

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 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/Singular_value_decomposition , https://en.wikipedia.org/wiki/Principal_component_analysis https://en.wikipedia.org/wiki/Principal_component_analysis that's probably the best and most "scientific" part of ML