I personally started with Kaggle competitions and lots of googling (duckduckgoing right?), but quite quickly hit the wall of not understanding, I felt like a mindless creature who makes a decision based on couple of guides out there. Watching lectures from Andrew Ng, reading some books helped a lot, but I can't see a reason why one doesn't wanna start with theory. It's no gold and glitter, and no one promised you that, unless you're really want to delegate your work to AutoML
I guess his point is to tackle it from a top-down approach. For me, that's how I am breaking ground in my ML study. I tried Andrew Ng's course, I didn't understand a thing.
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.
Same here. I tried Andrew Ng's course a few times ever since it launched a few years back but I could only get through half of it. Fast ai makes more sense to me and I've picked up a decent amount of concepts where I can now go back and feel confident enough to tackle theory.
