[atsheehan]

If anyone is interested in learning more about scikit-learn, I'd recommend "Hands-On Machine Learning with Scikit-Learn and Tensorflow" from O'Reilly:

http://shop.oreilly.com/product/0636920052289.do

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.

[a_bonobo]

There's also o'reilly's 'Introduction to Machine Learning with Python', which is very much about scikit-learn

http://shop.oreilly.com/product/0636920030515.do

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.

The Jupyter notebooks including the book's code are on GitHub: https://github.com/amueller/introduction_to_ml_with_python