In this tutorial, you use unsupervised learning to discover groupings and anomalies in data. Unsupervised learning is when there is no ground truth or labeled data set that shows you the expected result. Instead, you take the raw data and use various algorithms to uncover clusters of data. If you want to learn about the theory and ideas behind unsupervised learning.
Presentation about unsupervised algorithms and explaining their use cases based on clustering where it can be efficiently used. A hands on session which will allow you to see the outputs of algorithms such as k-means, mean shift, DBSCAN and agglomerative clustering. This tutorial will also include a self assessment which will allow you to test your knowledge at the end of the course .