It's useful to do learning on small toy problems for ease of debugging and speed of training, so if you want to learn how to apply a powerful technique, you're pretty much inevitably going to start learning it on something for which it's absolutely ineffective overkill. E.g. a common start for learning neural nets is a task like XOR which can be solved with literally a single machine instruction instead of training a ML model for it.

But also there are many tasks for which naive Bayes works, but a NN solution can be much more accurate if you're okay with it being also much more compute-intensive. E.g. things like sentiment analysis or simple spam filters are often used as a demonstration of naive Bayes, but you can do much better with more powerful models.

As with any super-hyped technology, people want to use the latest and greatest to solve it, even when traditional methods are cheaper, easier, more reliable and more accurate. See also: crypto.

Any data that is small enough to quickly iterate on for learning is small enough to use a simpler approach. The point is learning the techniques.