I feel the exact opposite. ML is a huge part of my job (or was), and I topped out at Discrete Math 2 or so, finishing up a few courses of the usual Calculus but

I feel the exact opposite. ML is a huge part of my job (or was), and I topped out at Discrete Math 2 or so, finishing up a few courses of the usual Calculus but never anything as esoteric as Real Analysis or the like. (I think I finished up some Linear Algebra.) Anyway, my math is not that great and I'm way out of practice when it comes to theoretical work. Not only that, I suck at algorithm design and I'm at best a moderately decent developer from a theory standpoint. But applied machine learning came natural, because I have a strong grasp of frequentist and Bayesian statistics as well as solving the problems backwards. If you try to apply ML or any other "exotic" solution by starting with "oh man here is this awesome technique," you will definitely be frustrated; I know that feeling all too well. ML is just another tool in the shed. With practice, you start to understand where simple linear regression techniques are useful and when more heavy-duty methods are applicable. Keep solving real world problems rather than trying to look for stuff to hit with your new hammer (ML in this case) and I bet you'll come around a lot quicker.