Last updated: November 13, 2025 by the editorial team
Author's): Alok Choudhary
Originally published in Towards Artificial Intelligence.
Unsupervised Machine Learning: The Complete Guide
Machine learning can be broadly divided into two categories: supervised learning AND unsupervised learning. While supervised learning deals with labeled data and the goal is to predict the outcomes, 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 discusses evaluation methods such as Silhouette Score for assessing the quality of clustering, making unsupervised learning essential to data science.
Read the entire blog for free on Medium.
Published via Towards AI
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