A lot of easy to digest content in this! Always great to see quality free material to help more people pick up machine learning and get involved solving problem

A lot of easy to digest content in this! Always great to see quality free material to help more people pick up machine learning and get involved solving problems using data science. One thing I didn't see covered in depth here was feature engineering, which is the process of preparing your raw data for the machine learning algorithms. They cover it briefly in the chapter on "Practical Considerations", but anyone looking to apply ML in the real world should look into feature engineering more on their own. One resource I recommend (and I am biased), is a python library for automated feature engineering called, Featuretools (https://github.com/featuretools/featuretools/ https://github.com/featuretools/featuretools/). It can help when your raw data is still too granular for modeling or comprised of multiple tables. We have several demos you can run yourself to apply it to real datasets here: https://www.featuretools.com/demos https://www.featuretools.com/demos.