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Not really. I get the dislike for the hyped up terms but there are many companies that work in ML/data science (and hire ML engineers or data scientists) that do know what they're doing. And there are distinctions, though sometimes subtle, between the terms.

Sure, I see what you mean, but he is trying to help people who are navigating "machine learning", whatever that is. Your argument is the same as "well, why did they call it gravity, shouldn't they have called it attraction?" It is what it is, and you teach based on the term already in the literature.

It's not really "magic". It's just that interpretation is not really that important sometimes. It's the approach of "Who cares what these coefficients mean, the model performs well on the test set". This is true for most of predictive modelling.