I cannot recommend scikit-learn enough to anyone interested in machine learning who likes python. I have been working with it for part of my Thesis, and it can do so much, with so little code. It is amazing.
When I first started using scikit-learn, I was overwhelmed with the number of classes and options available. I just chose some basic classifiers I was familiar with and stuck with most of the default settings. The book explains many of the other models and when they would be useful, but also spends a lot of time exploring the datasets (using pandas), preprocessing data and building data pipelines, finding the best hyperparameters, best ways to evaluate a models performance, etc. The library feels less like a big bag of algorithms now and more like a cohesive data pipeline.
Just came out a few weeks ago so it's relatively unknown, I've read the first few chapters and so far it's good! The first author Andreas Mueller is one of the scikit core devs.
I've been somewhat addicted to HackerRank challenges over the last couple of weeks. Why is not important, don't judge :)
The python packages and tooling around learning and science are truly amazing.
Try and do the Craigslist category classification without using python and see what I mean.
