AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

Unsupervised learning is a type of machine learning that involves training algorithms on datasets without labeled outcomes. In this approach, the model seeks to identify patterns or structures in the input data without predefined categories or labels guiding the learning process.

Clustering is a fundamental unsupervised learning technique that focuses on grouping similar data points together based on their characteristics. The goal of clustering is to discover natural groupings within the data, such as identifying customer segments in marketing or finding clusters of similar documents in text analysis. Since clustering algorithms do not rely on labeled data for training, they exemplify the unsupervised learning paradigm effectively.

The other techniques mentioned—classification, regression, and decision trees—are primarily associated with supervised learning, where models learn from labeled data to make predictions or decisions based on known outcomes.