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Believe it or not this stuff is easy mode once you learn to ignore the terminology. It takes relatively little knowledge to know how to get data into the right format, set sensible training parameters and check results -- basically a handful of function calls. The trained models are also highly interpretable, you can literally plot them with Graphviz to understand how the model is treating values of each feature ("column")

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It's so easy to understand if every step is dumbed down and visualized. Teachers who write dense math on whiteboards need to learn a thing or two about pedagogy. The math should supplement the pictures, not the other way around, unless your audience is other experts.

Easy to understand but hard to master. Any slip mixing training data in validation or testing data and your metrics are useless. It can be difficult to impossible to spot and give newbies false confidence in their model.

The same is true pretty much everywhere else (cryptography, sport, electrical engineering, aeronautics, ..). There is no greater teacher than a head injury or an electrical fire, the question is whether OP's fear is warranted or whether it should be encouraged. Perhaps they should not set the bit rate on a video codec until completely understanding the fundamentals of that technology too? I'd suggest like in many similar areas, gatekeeping is a not insubstantial force

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