Unsupervised Learning

Clustering, dimensionality reduction, PCA, t-SNE, UMAP.

Course Duration: 6 hours

What You'll Learn

• Clustering algorithms
• Dimensionality reduction
• Anomaly detection
• Association rules
• Unsupervised evaluation metrics

Prerequisites

• Supervised Learning basics
• Python and NumPy

Course Modules

1. Introduction to Unsupervised Learning
2. K-Means Clustering
3. Hierarchical Clustering
4. DBSCAN
5. Gaussian Mixture Models
6. Principal Component Analysis (PCA)
7. t-SNE Visualization
8. UMAP
9. Anomaly Detection
10. Association Rules (Apriori)
11. Project: Customer Segmentation

Algorithms Covered

• K-Means / K-Medoids
• Hierarchical (Agglomerative)
• DBSCAN / HDBSCAN
• PCA / Kernel PCA
• t-SNE / UMAP