Chapter 2
Discovering Hidden Structures with Unsupervised Learning
Section 1
Understanding Unsupervised Learning and k-means Clustering
Unsupervised learning can be immensely helpful, for example, as a preprocessing or feature extraction step. You can think of unsupervised learning as a data transformation—a way to transform data from its original representation into a more informative form. In this video, we will understand k-means Clustering and we will implement our first k-means example. - Generate a 2D dataset containing four distinct blobs - Create 300 blobs belonging to four distinct clusters - Produces scatter plot of all colored data points