Interpretable Machine Learning I

In this blog post, I am covering Christoph Molnar's Interpretable Machine Learning Book. During the reading process, I took copious notes, which are partly take-aways from the book for my personal Wiki and my personal notes on the topic. Using these notes as a template, this blog post turned into a rather strange hybrid between book review, remarks, and tutorial. It's by no means exhaustive, and I recommend reading the book itself if you want to learn about machine learning and interpretability. This has been going fairly well, having read 20 books in 2020 so far, that's been going pretty well.