Class 14: Unsupervised Machine Learning and Clustering

Summary: Explore unsupervised learning techniques for pattern discovery, grouping, and dimensionality reduction.

Learning Objectives:

• Apply clustering algorithms to segment data
• Implement dimensionality reduction techniques
• Interpret unsupervised learning results

Key Topics:

• K-means and hierarchical clustering algorithms
• Principal Component Analysis (PCA) fundamentals
• Association rule mining and market basket analysis
• Anomaly detection methodologies

Activities:

• Customer segmentation project
• Dimensionality reduction visualization
• Pattern discovery in complex datasets