[1] https://en.m.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effec...
However, in a business setting, starting from practice is much more effective. As a lead dev and a manager who's had over 20 years of experience in AI/ML I've trained several engineers in building ML systems.
I always start with a business problem and point them to resources (frameworks, blogs, jupyter notebooks) to help them along. The problem is small enough for them to solve in less than a quarter. I avoid micromanaging them and will only answer larger questions by providing more resources. If they really get stuck I'll sit with them and walk through the issue. I have yet to have an engineer be unable to 1) get a model working and 2) tune it to production quality.
Very few people can read about bias variance trade off and in the course of using a model, understand how to take that concept and directly apply it to the problem they are solving. In retrospect, they can look back and understand the outcomes. Also, most theory is useless in the application of ML, and only usefull in the active research of new machine learning methods and paradigms. Courses make the mistake of mixing in that useless information.
The same thing is true of the million different optimizers for neural networks. Why different ones work better in different cases is something you would learn when trying to squeeze out performance on a neural network. Who here is intelligent enough to read a bunch about SGD and optimization theory (Adam etc), understand the implications, and then use different optimizers in different situations? No one.
I'm much better off having a mediocre NN, googling, "How to improve my VGG image model accuracy", and then finding out that I should tweak learning rates. Then I google learning rate, read a bit, try it on my model. Rinse and repeat.
Then I tried Kaggle's mini-course. It kickstarted me into ML and motivated me to learn the theory as I go. For example, when I got to apply Random Forest Regressor, I went to Wikipedia and tried to read on it. Got some idea. And the progress is good.
Maybe for some of us, I think top-down is motivating and makes the learning process enjoyable.
One cannot do ML without some basic theoretical knowledge of Statistics and Probability. This gives you the What and the Why behind everything. GI-GO is more true of ML than other disciplines. The techniques used are so opaque that if you don't know what you are doing, you can never trust the results.
The best approach is to learn both concurrently. Learn some theory, apply it and understand that applications including pitfalls, then learn a bit more and repeat. Incremental learning with a solid base. It's fun to hate on academia but this is how experts with deep knowledge of a domain get to where they are.
That said, playing for 1-2 weeks might be a good start towards getting motivated for learning the difficult and dry theory needed to excel in this field.
Which, well, I use as an opening quote to my intro to deep learning, https://github.com/stared/thinking-in-tensors-writing-in-pyt....
BTW: While information theory is everywhere, I have to yet see where measure theory makes a practical impact on practical deep learning. The importance of pure math for practical machine learning is highly overrated (and I speak as someone who did study that).