Unsupervised Learning I#
This first chapter on unsupervised learning will focus on clustering. Clustering is a type of unsupervised learning that aims to discover groupings or patterns in data. Unlike supervised learning, in which the model is trained on labeled data to make predictions, unsupervised learning involves finding structure in data without any prior labeling. Clustering algorithms attempt to divide the data into meaningful subgroups or clusters, such that the data points within each cluster are more similar to each other than to those in other clusters. This allows us to discover hidden patterns and relationships within the data, and to understand the structure of the data in a way that is not possible with other machine learning techniques. Clustering algorithms have many practical applications, such as image segmentation, customer segmentation, and anomaly detection, among others.
Where to Learn More#
I’ve covered clustering in-depth in the following course: