Unsupervised Machine Learning: A Complete Guide

Author(s): Alok Choudhary

Originally published on Towards AI.

Unsupervised Machine Learning: A Complete Guide

Machine Learning can be broadly divided into two categories: supervised learning and unsupervised learning. While supervised learning deals with labeled data, where the goal is to predict an output, unsupervised learning deals with unlabeled data.

The article provides an overview of unsupervised machine learning, detailing its key algorithms such as K-Means, Hierarchical Clustering, and DBSCAN, each with unique strengths for different clustering tasks. It discusses practical applications including customer segmentation and anomaly detection, highlights the importance of data patterns in making decisions without labeled inputs, and addresses evaluation methods like the Silhouette Score to assess the quality of clustering, thus making unsupervised learning essential for data science.

Read the full blog for free on Medium.

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Published via Towards AI