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Summary here
Module · 5 lessons
Unsupervised Learning
Discover structure in unlabeled data with k-means clustering and segmentation.
At a glance
Level
Intermediate
Lessons
5 lessons
Time to complete
1–2 weeks
Cost
Free forever · no sign-up
Not all data comes with labels. Unsupervised learning finds hidden structure on its own. In this module you will learn k-means clustering from the iterative algorithm up, how to choose the right number of clusters, and how to apply clustering to real business segmentation problems.
Lessons in this module
1
Introduction to Unsupervised Learning
Learn what unsupervised machine learning is and how clustering finds hidden structure in unlabeled data using the real Mall Customers dataset
2
The Iterative K-Means Algorithm
Learn how k-means clustering works step by step by building the assign-and-update loop from scratch on real customer data
3
Choosing the Number of Clusters with the Elbow Method
Learn how to choose the right number of clusters for k-means using the elbow method and the silhouette score on the real Mall Customers dataset
4
K-Means with Scikit-Learn and Interpreting Results
Run K-Means end to end with scikit-learn and turn the resulting clusters into a clear business story
5
Guided Project: Wholesale Customer Segmentation
Run a full clustering project end to end: segment wholesale distributor clients by their annual spending across six product categories
Achievement
Complete all 5 lessons to finish the Unsupervised Learning module.