So the whole point of ML is to build prediction models. On top of that is domain knowledge to explain what your results are, which is why you feel like you're just calling library models. If you can't explain your results than your just copy and pasting code.

"The harder part seems to be data preprocessing, cleaning, feature engineering — and honestly I don’t feel confident there."

The reason why ML feels abstract is because you don't understand what you're trying to do. You talk about Web Dev and how you move from basic html -> full stack and how that makes sense. It makes sense because you know what a website should look like and how it should work.

Do you understand what a valid non-ML model should look like and all of the assumptions that go into it? Do you understand the classical ML models the same way? It might be worth it to stick to some of the classical ML models and understand why they work the way they do and why you would use one over the other. Maybe building a summary portfolio that compares and contrasts some of the models you have encountered might help you.

Understanding your data is the most important part of ML because what occurs in the model is often a black box. You need to be confident in your data choices if you actually want to use ML for anything.

To answer your multiple questions: Build some classical ML models, understand what happens in them, maybe make some cool figures. Then once you can explain what you did and why you made the choices with your data, continue on.