A very brief introduction to what ML is for and the problems to which it can be applied, along with some example applications.

A glossary of terms used in machine learning. For an up-to-date glossary, also check https://developers.google.com/machine-learning/glossary.

Hyperparameter tuning is the process of finding the optimal hyperparameters (the parameters fed into the model for it to change its inner parameters) for a model to converge efficiently through trial and error.

Expectation maximization is a method of finding maximum likelihood estimates of the parameters of a model. The method alternates between making an expectation (E) step based on the current estimate of the parameters and a maximization (M) step, which computes new parameters.