Explains how unsupervised machine learning's purpose is to create new groups to put data into, and the clustering types used in this process.

An example of how a pizza restaurant would use the k-means method of machine learning and centroids to find new groups of customers.

Illustrates how once-separate diagnoses were placed together under the diagnosis of autism spectrum disorder thanks to heirarchical clustering, resulting in better treatment regimens.

Explains how heirarchical clustering finds smaller subgroups within larger differentiated clusters, and how these groups can be visualized on a dendogram.

Defines and gives some brief examples of neural networks.