I didn't double major in math or stats. I squeaked through linear algebra without understanding the material, and then re-studied it independently a few years

I didn't double major in math or stats. I squeaked through linear algebra without understanding the material, and then re-studied it independently a few years ago and actually understood a little more. Yet, I can still apply machine learning to solve Ax=b problems. More importantly, I can use business analysis and write code to transform business problems into an Ax=b problem, and then optimize it. You don't need a PhD to grok optimizing a vector to transform a matrix of inputs into a vector of observed outputs, then apply that trained vector going forward. Neural nets are slightly more complicated than a straight linear regression, but only slightly. I'd call decision tree methods like GBM even more complex, but still eminently grokkable for a decent programmer.