For any arbitrary range of values for $DATA_TO_COLLECT and $ML_MODEL? Maybe not. For many real world scenarios? I'd say yes. Heck, sometimes the data you have available simply "is what it is" and there is no question of "what data to best collect".

Let me also add re-iterate that learning to use ML is a continuum... and I would not posit that one could take Andrew's class, or the fast.ai class, and claim to be "done" with their ML education. To me either (or both) of those is just step one on a journey that could continue more or less forever (or until ASI comes along and enslaves the human race, ala The Matrix).

Personally I took Andrew's (original) Coursera course, but since then I've continued to study Linear Algebra and Calculus through other forums, have been working through several other ML books, and have a whole litany of stuff queued up to study, up to and including the aforementioned Measure Theory, Complex Analysis, etc. I definitely value that stuff, I just think you can accomplish some useful tasks before you get to that level.

Once you get your hands dirty, and need to debug unexpected issues, understanding what happens under the hood becomes more important.