Chapter 8: Unsupervised Learning¶

Discover hidden patterns in unlabeled data—clustering, dimensionality reduction, and anomaly detection.

Metadata¶

Learning Objectives¶

• Implement K-Means clustering from scratch using NumPy
• Apply hierarchical clustering and interpret dendrograms
• Use DBSCAN for density-based clustering with noise detection
• Evaluate clusters with silhouette scores and the elbow method
• Reduce dimensionality with PCA and t-SNE
• Detect anomalies with Isolation Forest and statistical methods
• Build a complete customer segmentation pipeline

What's Included¶

Notebooks¶

Scripts¶

• unsupervised_toolkit.py — Core implementations (KMeansScratch, PCAScratch) and plotting utilities

Exercises¶

• 5 exercises with solutions (in solutions/ branch)

SVG Diagrams¶

• 3 visual diagrams for clustering algorithms, dimensionality reduction, and anomaly detection

Read Online¶

You can read the full chapter content right here on the website:

• 08.1 Introduction -- K-Means from scratch, silhouette scores, elbow method
• 08.2 Intermediate -- Hierarchical clustering, DBSCAN, Gaussian Mixture Models
• 08.3 Advanced -- PCA, t-SNE, anomaly detection, customer segmentation capstone

Or try the code in the Playground.

How to Use This Chapter¶

1. Clone and install dependencies

git clone https://github.com/luigipascal/berta-chapters.git
cd berta-chapters
pip install -r requirements.txt

2. Navigate to the chapter

cd chapters/chapter-08-unsupervised-learning

3. Launch Jupyter

jupyter notebook notebooks/01_introduction.ipynb

Created by Luigi Pascal Rondanini | Generated by Berta AI