Getting started with unsupervised machine learning
Unsupervised machine learning is a fascinating branch of artificial intelligence that focuses on discovering patterns, relationships, and structures within data without explicit guidance from labeled outcomes. Unlike supervised learning, where models are trained with labeled data to make predictions, unsupervised learning aims to explore the inherent information present in the data itself. This type of learning is particularly valuable for uncovering hidden insights, finding clusters, reducing dimensionality, and revealing underlying representations. Clustering is a common use case for unsupervised learning.
Clustering refers to grouping data points into distinct subsets or “clusters” based on similarities in their features without using pre-labeled data as a guide. Imagine that you have a scatter plot of data points and want to color-code groups of points that seem to cluster together; this is essentially what clustering...