ML Book 3 - Unsupervised Learning and Dimensionality Reduction for Entry-level

Table of Contents

1. Introduction

1.1 What is Unsupervised Learning?

1.2 Why Unsupervised Learning Matters?

1.3 Types of Unsupervised Learning Problems

1.4 Challenges and Limitations of Unsupervised Learning

1.5 Applications and Examples of Unsupervised Learning

2. Clustering

2.1 What is Clustering?

2.2 How to Measure Cluster Quality?

2.3 Popular Clustering Algorithms

2.4 Project: Customer Segmentation with K-Means

2.5 Project: Image Compression with K-Means

2.6 Project: Topic Modeling with Latent Dirichlet Allocation

3. Dimensionality Reduction

3.1 What is Dimensionality Reduction?

3.2 Why Dimensionality Reduction Matters?

3.3 Types of Dimensionality Reduction Techniques

3.4 Project: Feature Selection with Variance Threshold and Correlation Matrix 3.5 Project: Feature Extraction with Principal Component Analysis

3.6 Project: Feature Extraction with Autoencoders

4. Anomaly Detection

4.1 What is Anomaly Detection?

4.2 How to Define and Measure Anomalies?

4.3 Popular Anomaly Detection Algorithms

4.4 Project: Credit Card Fraud Detection with Isolation Forest

4.5 Project: Network Intrusion Detection with One-Class Support Vector Machine 4.6 Project: Outlier Detection with Local Outlier Factor

5. Association Rule Mining

5.1 What is Association Rule Mining?

5.2 How to Measure Association Rules?

5.3 Popular Association Rule Mining Algorithms

5.4 Project: Market Basket Analysis with Apriori

5.5 Project: Movie Recommendation System with FP-Growth

5.6 Project: Web Usage Mining with PrefixSpan

6. Conclusion

6.1 Summary of Key Concepts and Techniques

6.2 Future Trends and Directions of Unsupervised Learning

6.3 Resources and References for Further Learning