Yours is a very applied approach; I would say that about 5% of all AI engineers know what PAC learning is. But of course its them who develop those huge frameworks and libraries like tensorflow, keras or caret.
It depends on what you want. Do you want to gain more insight into the blackbox called nature or just want to use it to provide good predictions without knowing why your model does that?
Especially in algorithmic modelling it takes significant extra effort to understand the underlying principles of probability theory.
The huge benefits you get out of it:

• brain training and
• an exact number of how big your dataset has to be to guarantee an error rate with a certain probability.

It's worth it. Never give up at math.

I like your posts. They inspire me. ...second day for me on this platform. Just great to meet some fellas here.