Unsupervised Learning

What You’ll Learn in This Series

This tutorial series covers key unsupervised learning techniques used for data exploration, dimensionality reduction, clustering, and pattern discovery. You’ll get practical examples, Python code, and real-world use cases.

Tutorial Topics:

1. Introduction to Unsupervised Learning
2. K-Means Clustering Algorithm
3. Hierarchical Clustering
4. Principal Component Analysis (PCA)
5. Autoencoders for Dimensionality Reduction
6. Gaussian Mixture Models (GMM)
7. Association Rule Learning (Apriori, FP-Growth)
8. DBSCAN Clustering Algorithm
9. Self-Organizing Maps (SOM)
10. Applications of Unsupervised Learning