I personally think it is possible to get a grasp of how many ML models learn, if you can get the intuition behind it - without the formal math knowledge, but only up to a certain point.
From my time in college studying this, you had approximately four types of students:
1) Those that didn't understand how models worked, and lacked the math to theoretically understand the models (dropped out class after a couple of weeks)
2) Those that understood (intuitively) how the models worked, but lacked the math to read and formalize models. Lots of students from the CS program fell under this group - but I think that is due to CS programs here having less math requirements than traditional engineering and science majors.
3) Those that understood how the models worked, and had the math knowledge. This was the majority of students.
4) Those that did not understand the models, but had the math knowledge.
Of these, 2-3 were the most common types of students. In the rare occasion, you had type 4 students. They would have no problem with deriving formulas, or proving stuff - but they'd more or less freeze up or start to stumble when asked to explain how the models worked, on a blackboard.
With that said, if someone has any ambition of doing ML research, I think math prereqs are a must. Hell, even people with good (graduate level) math skills can have a hard time reading papers, as there are so many different fields/branches of math involved. Lots and lots of inconsistent math notation, overloading, and all that.
There's a lot of contrived "mathiness" in papers, even where simple diagrams will do the trick. If your paper doesn't include a certain amount of equations / math, people aren't taking it serious...so some authors will just spam their papers with somewhat related equations, using whatever notation they're most comfortable with.