Module 08: Unsupervised Learning

Learn to find patterns in data without labels.

What You'll Learn

• Clustering Algorithms (K-Means, Hierarchical, DBSCAN)
• Dimensionality Reduction (PCA, t-SNE)
• Anomaly Detection
• Association Rules
• Real-world Applications

Advanced machine learning curriculum map

Topics Covered

1. Clustering

• K-Means: Partition data into k clusters
  • Choosing k (Elbow method, Silhouette score)
  • Pros and cons
• Hierarchical Clustering: Tree-like cluster structure
  • Agglomerative vs Divisive
  • Dendrograms
• DBSCAN: Density-based clustering
  • Handles non-spherical clusters
  • Identifies outliers

2. Dimensionality Reduction

• PCA: Linear dimensionality reduction
  • Explained variance
  • When to use
• t-SNE: Non-linear visualization
  • Great for visualization
  • Not for feature reduction
• UMAP: Modern alternative
  • Faster than t-SNE
  • Better global structure

3. Anomaly Detection

• Isolation Forest: Tree-based anomaly detection
• One-Class SVM: Support vector approach
• Local Outlier Factor (LOF): Density-based
• Applications: Fraud detection, system monitoring

4. Association Rules

• Market Basket Analysis: Find item associations
• Apriori Algorithm: Find frequent itemsets
• Support, Confidence, Lift: Key metrics

Learning Objectives

By the end of this module, you should be able to:

• Apply clustering algorithms to unlabeled data
• Reduce dimensionality for visualization
• Detect anomalies in data
• Find associations in transactional data

Projects

1. Customer Segmentation: Cluster customers by behavior
2. Anomaly Detection: Detect fraudulent transactions
3. Market Basket Analysis: Find product associations
4. Data Visualization: Use t-SNE to visualize high-dim data

Key Concepts

• No Labels: Unsupervised learning works without targets
• Clustering: Group similar data points
• Dimensionality Reduction: Reduce features while keeping information
• Anomaly Detection: Find unusual patterns
• Evaluation: Harder without labels (use silhouette score, etc.)

Documentation & Learning Resources

Official Documentation:

• Clustering - Scikit-learn
• Dimensionality Reduction - Scikit-learn
• Anomaly Detection - Scikit-learn

Free Courses:

• Unsupervised Learning (Coursera): Week 8 of Andrew Ng's course
• Clustering (Kaggle Learn): Free micro-course

Tutorials:

• K-Means Clustering Tutorial
• PCA Tutorial
• t-SNE Explained
• Anomaly Detection Guide

Video Tutorials:

• K-Means Clustering (StatQuest)
• Hierarchical Clustering (StatQuest)
• PCA (StatQuest)
• t-SNE (StatQuest)

Practice:

• Customer Segmentation Projects
• Clustering Exercises

Complete Detailed Guide →

Additional Resources:

• Advanced Topics →: Advanced clustering, anomaly detection, association rules
• Project Tutorial →: Step-by-step unsupervised learning project
• Quick Reference →: Quick lookup guide for unsupervised learning

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