Overview
Curriculum
Discover how unsupervised learning helps uncover hidden patterns in data without labels. This module introduces key concepts of clustering, with a focus on the k-means algorithm. We’ll learn how k-means works, when to use it, and how to choose the right number of clusters for meaningful insights from raw data.
What You'll Learn
- Discuss the basics of unsupervised learning and how it differs from supervised learning
- Explain the concept and steps of the K-means clustering algorithm
- Apply K-means to group unlabeled data and uncover patterns
- Evaluate the strengths and limitations of K-means clustering
- Determine the optimal number of clusters using the elbow method
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