Unlike some of the other complicated tools, sklearn is just a "pip install" away and includes all sorts of examples of different problems. Classification? Regression? Clustering? Representation learning? Perceptual embedding? Odds are, some part of sklearn covers all of that.
The scikit-learn tutorials are great. Another nice thing about scikit-learn is that the api for a lot of different ML algorithms is very similar, almost identical.
This means that you can set up a train and and test set and swap in and out random forest, svm, naive bases, logistic regression, and various others.
Read about them one by one, try to understand the algorithms generally, test them out, see how they perform differently on different data sets.
It all depends on how you like to approach a new subject, but I think this is more fun and motivating than going straight into the mathematics behind the algorithms right away (which is more along the lines Andrew Ng's excellent course). I'd say once you're into it and using the algorithms, then dig deeper into the core mathematics, you'll have a better context for it.
Unlike some of the other complicated tools, sklearn is just a "pip install" away and includes all sorts of examples of different problems. Classification? Regression? Clustering? Representation learning? Perceptual embedding? Odds are, some part of sklearn covers all of that.