We had to go through the following article two, three, four times and recreate it ourselves and then explain all the principles to our fellow students. This helped me a lot in understanding the math behind it.
https://towardsdatascience.com/netflix-and-chill-building-a-...
PS. I'm a sucker when it comes to math. When I got the infamous math questions with metaphorical elephants on weight scales I couldn't stop but wonder why the elephant was on the scale.
It'll take ages before you need maths more complicated than arithmetic.
Taking a stats course won't help you at all. The way canned ML algorithms work has nothing whatsoever to do with data generating models which is what stats is about.
You'll start to need maths when it comes to dimensionality reduction and embedding and that'll come in the form of 95% linear algebra and 5% calculus. Still no stats though.
Wayyyy down the line if you get into the information theoretic view, or pick up the fetish of justifying everything via a Bayesian methodology then you'll need lots of stats.
That shouldn't stop you from doing kaggle competitions right now though if thats' what you like. Personally I dislike kaggle but there's no reason why you can't just grab a dataset, experiment with different algorithms and learn as you go.
You may have to learn a little multivariable calculus, or at the very least look at enough examples of its notation to be able to comfortably skim equations