Unsupervised Learning with Python
Core unsupervised ML with scikit-learn — K-Means, internal metrics, PCA, and real segmentation projects across 23 lessons and 184+ drills.
Who it's for
Analysts who know supervised ML and need to segment academy cohorts and shop customers when no target column exists.
What you'll learn
- Build X-only workflows for unlabeled academy and shop data
- Fit, interpret, and assign K-Means clusters including new points
- Choose k with elbow, silhouette, Calinski-Harabasz, and Davies-Bouldin in one combined workflow
- Apply PCA for visualization and variance-based component selection
- Deliver academy and shop segmentation projects
Course contents
- 01Foundations · 5 lessons
- 02K-Means Clustering · 6 lessons
- 03Cluster Evaluation · 4 lessons
- 04PCA & Dimensionality · 4 lessons
- 05Practice & Capstone · 4 lessons