[rjvir]

Different people learn in different ways, but personally I’ve had more success with the opposite approach, ie “top-down”.

As in, rather than learning in depth all the low level parts then finally putting it together at the end, start with a surface high-level understanding of a working prototype then expand into the details of how everything works inside.

In the case of ML, this could mean starting with a 5 line SciKit-learn prototype of a random forest model, seeing some working predictions, then expanding knowledge from there - what data is going in and what is coming out? What’s a classifier? What’s a decision tree? Etc

[antipaul]

I support this learning method. Having a map of the key concepts, as well as visceral feel for them via code, will keep up the motivation.

This would be in contrast to picking up one of the plethora of “ML” textbooks that mostly only describe the math behind all the algorithms. Which is not where you should begin, in my view (years of teaching experience). The use of such textbooks is as a reference to fill in details once your are curious about them.

And more than anything, the best way to learn practical ML is to “apprentice” to some experienced practitioners or team who are willing to act as mentors.

[astrophysician]

Agree — people should do what they want and have fun learning. Its just a suggestion that’s also colored by my own experience. I will argue that if you’re going for a job in data science it is a bit more of a serious suggestion since you need to be able to know how to answer statistical questions and understand your assumptions, and you really do need to understand Bayesian stats for that (nothing state of the art here, don’t worry if you don’t know what a PGM is, I just mean basics).

[rangerranvir]

Great Point! I do read article where someone builds such cool stuff every now and then to keep in mind what's coming.

Anyway whatever works. Ultimate aim is to learn and have fun.