I should probably have not mentioned the academic use case. My advice is better for people trying to solve real-world problems, as opposed to improving the theory. Not to say that improving the theory doesn't later lead to massive real-world solutions!

I feel very strongly, that if someone wants to apply ML to accomplish something outside of academia, they should think about an applied use case and then work backwards to what they need to learn. Otherwise, you will have a solution in search of a problem.

If someone wants to go into academia or theory, then yes, I think you're right. They need to pick a research area. But then I think the goal should be get to the problem space as soon as possible. I think it would be suboptimal to decide "I want to improve Bayesian ML" without first deciding the "why", such as: "I want to make ML models more understandable." And yeah, maybe you need to do a little research to know what the problems-to-solve are first.

Per all the above, I never went into academia, so take my opinion there with a grain of salt. I have worked exclusively at startups and co-founded one, so take my opinion there with two grains of salt :)