Explains how to train machine learning algorithms with training and testing data sets, and the confusion matrix method of analysis.

Exemplifies and explains when logistical regression machine learning is useful, in this case for micro loaning.

Explains the K-Nearest Neighbors method of machine learning using the heights of dog breeds. This method assumes that data points that are close together are similiar.

Explains how linear discriminate analyses use Bayes' Theorem using the example of predicting college admissions given certain conditions.

Defines supervised machine learning and describes some of the uses in which it is better for modeling than other statstical models.