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

Unsupervised learning is a type of machine learning where the algorithm is given data without explicit instructions on what to do with that data. The primary goal is to identify patterns or groupings within the data based solely on its inherent structures.

Segmenting customers into groups exemplifies a suitable application of unsupervised learning. In this scenario, the algorithm analyzes customer data to find natural clusters or segments without any prior labels or categories assigned to the data. For instance, it may discover that certain customers have similar purchasing behaviors or demographics, leading to the formation of distinct market segments. This information can then be leveraged for targeted marketing strategies, product recommendations, or overall business intelligence.

In contrast, the other options typically require labeled data or prior knowledge to train a model effectively. Detecting fraudulent transactions often relies on historical examples of known fraud to train a classifier, making it a supervised learning task. Predicting stock prices typically involves regression algorithms trained on historical data, which is again a supervised approach. Identifying spam emails usually utilizes labeled datasets of spam and non-spam messages to create a classification model, placing it also in the realm of supervised learning. Thus, the application of segmenting customers aligns perfectly with the core principles of unsupervised learning.