The math is important for understanding and tweaking. But ML is not just fancy statistics (or math in general). It's _data_. An understanding of the math means understanding what data pairs best with what approach. It also means understanding error analysis. It means cross-validation. These are far more vulnerable areas to a beginner in ML.
But the ultimate point is ML is not magic. It's a framework - trenched in mathematics - that provides the building blocks for simulating understanding. You don't need to know the math to use it, but you need it to use it well.
As a data scientist, I often walk new clients through a linear regression exercise to convey some key concepts about the engagement and demystify what I'll be doing for them.
I'm often dealing with people who, much like you, haven't done much with stats since a college "Business Stats" course, so I get a lot of "oh yeah, I vaguely remember this" - but going through it again gives me a good foundation to relate back to as things get more complicated.
The first couple weeks are all about univariate and multivariate linear regression (as well as an optional linear algebra refresher on matrix operations).