People generally recommend "Machine Learning" by Andrew Ng on Coursera. It's in Octave, but people have done it in Python:
E.g. this person:
Could also try this chunky specialisation by some Russian uni:
Like the other guy said, read sklearn documentation if you want to do Python. But instead of reading it as its own thing, I'd say use it as a reference personally. But that's maybe because I learn better with videos.
If you want to try and understand Bayesian shit, look here:
This guy posted a "super harsh" guide:
For supplementary videos on understanding a topic, you could look up "Machine Learning Summer School" on YT and search through looking for things you might find interesting.
E.g. 2 videos on Gaussian Processes by Neil Lawrence, below is part 1 (but seems shite according to the comments because of some slide issues, but this is just an example)
I suppose it depends what you want to learn. What's your endgame here? Like what is the sort of job you're after?