Scikit-learn user guide (2017) [pdf]

I agree that this text is great for machine learning theory, but often times non-students learn better through learning tools while solving real problems, rather than focusing on theory. Scikit-learn is an amazing tool for facilitating problem based machine learning. I don’t feel the same way about R unless the student has a statistics degree already...

The entire scikit-learn Documentation in a single PDF.

sklearn (scikit-learn) is a machine learning library for Python. In practice, it's useful for integrating a whole zoo of machine learning models for a range of tasks (supervised, unsupervised) and varying strategies within these domains (e.g., decision-tree based models, regression, neural networks) into a simple API, where the following three lines of Python code do 80% of the ML work for you.

>>> model = [insert sklearn class here]

>>> model.fit(trainX, trainY)

>>> model.predict(testX)

You can bring it up as an issue on github. They can probably move it down to the bottom.

The change log has A LOT of value if you are trying to figure out what version you should upgrade to. If you are stuck on an older version, then you need to understand what value you gain by potentially introducing new bugs into your product.

They also credit everybody who has worked on the project, which is super important.