I wonder whether something like this (albeit a little advanced, considering the field's depth) exists for machine learning?
There's also "Grokking Machine Learning" by Serrano, which I haven't read, but seems relevant to your question as well
> Discover valuable machine learning techniques you can understand and apply using just high-school math.
ML math, depending on how far you want to go, builds off of statistics, probability, linear algebra, basic calculus, information theory. There's more things that it builds off of, and they're more advanced topics that people don't get as much exposure to without going out of their way. ML is usually a lot harder to build intuition about, since so much of it doesn't have real world analogies that can easily tie together the math with the application.
There's a lot of individual pages scattered around the web that try to explain specific concepts in ways like this godot vector math page does (gradient descent tutorials come to mind), but there's no central repository or unified intuition because the scope is so broad. As the other guy mentioned, things like Andrew Ng's course are probably the best you're going to get if you want a decent understanding of the concepts.