Unsupervised Learning

The Core of Unsupervised Learning

At its core, unsupervised learning is about discovering hidden patterns and structures in the data without the need for labeled examples. Unlike supervised learning, where the algorithm is given labeled examples to learn from, unsupervised learning requires the algorithm to find the underlying patterns and relationships in the data on its own.

One of the most important techniques in unsupervised learning is clustering, which involves grouping similar examples together based on some similarity metric. Clustering can be used for a wide range of applications, such as market segmentation, anomaly detection, and image segmentation. By grouping similar examples together, we can gain insights into the underlying structure of the data and make more informed decisions.