luigipascal · cursor · Mar 6, 2026 · Mar 6, 2026 · Mar 6, 2026 · Mar 6, 2026
diff --git a/ABOUT.md b/ABOUT.md
@@ -155,7 +155,7 @@ Both exist simultaneously, creating a living curriculum.
 ### Near-term (2026)
 - ✅ Launch master repository (this!)
 - ✅ Complete Foundation Track (5 chapters — all available!)
-- 🔄 Release Practitioner Track (2 of 10 chapters available)
+- 🔄 Release Practitioner Track (3 of 10 chapters available)
 - 🔄 Establish community request process
 - 🔄 Build 100+ community-contributed chapters
 
@@ -178,11 +178,11 @@ Both exist simultaneously, creating a living curriculum.
 ## 📊 By The Numbers
 
 **Current State:**
-- 7 chapters available (Foundation complete + Practitioner started)
+- 8 chapters available (Foundation complete + Practitioner started)
 - 21 Jupyter notebooks with interactive content
 - 21 professional SVG diagrams
 - 37 exercises with solutions
-- 56 hours of learning content available
+- 64 hours of learning content available
 - 5 practice datasets
 - 25+ total chapters planned
 - $0 barrier to entry

diff --git a/GITHUB_PROFILE_README.md b/GITHUB_PROFILE_README.md
@@ -10,7 +10,7 @@
 
 **[Berta AI](https://berta.one)** — AI-powered tools for tomorrow's world
 
-- **[Berta Chapters](https://github.com/luigipascal/berta-chapters)** — Free, open-source AI curriculum. 7 chapters live, 25 planned. Learn Python to production ML through interactive notebooks, exercises, and an online playground. No paywall, no signup.
+- **[Berta Chapters](https://github.com/luigipascal/berta-chapters)** — Free, open-source AI curriculum. 8 chapters live, 25 planned. Learn Python to production ML through interactive notebooks, exercises, and an online playground. No paywall, no signup.
 - **[LLM Cost Optimizer](https://llm.berta.one)** — Cut LLM API costs 80-95% while keeping data private. Local processing, text anonymization, automatic model routing.
 - **OrbaOS** — A framework for post-project work. AI handles coordination so teams focus on strategy and creative output.
 

diff --git a/README.md b/README.md
@@ -53,7 +53,7 @@ Apply what you've learned to real-world machine learning and AI problems.
 |---------|-------|------|--------|
 | 6 | [Introduction to Machine Learning](./chapters/chapter-06-intro-machine-learning/) | 8h | ✅ Available |
 | 7 | [Supervised Learning: Regression & Classification](./chapters/chapter-07-supervised-learning/) | 10h | ✅ Available |
-| 8 | Unsupervised Learning: Clustering & Dimensionality Reduction | 8h | 🔄 Coming Soon |
+| 8 | [Unsupervised Learning: Clustering & Dimensionality Reduction](./chapters/chapter-08-unsupervised-learning/) | 8h | ✅ Available |
 | 9 | Deep Learning Fundamentals | 12h | 🔄 Coming Soon |
 | 10 | Natural Language Processing Basics | 10h | 🔄 Coming Soon |
 | 11 | Large Language Models & Transformers | 10h | 🔄 Coming Soon |
@@ -268,7 +268,7 @@ pie title Curriculum Breakdown
     "Community Requested" : 999
 ```
 
-- **Chapters Available Now**: 7 (56 hours of content)
+- **Chapters Available Now**: 8 (64 hours of content)
 - **Total Planned Chapters**: 25+
 - **Jupyter Notebooks**: 21 interactive notebooks
 - **SVG Diagrams**: 21 professional diagrams

diff --git a/ROADMAP.md b/ROADMAP.md
@@ -8,11 +8,11 @@ Our vision for the future of AI education. This is a living document—prioritie
 
 **Master Repository**: ✅ Live  
 **Foundation Track**: ✅ Complete (5 chapters available)  
-**Practitioner Track**: 🔄 In progress (2 of 10 chapters available)  
+**Practitioner Track**: 🔄 In progress (3 of 10 chapters available)  
 **Advanced Track**: 📋 Planned (10 chapters)  
 **Community Requests**: 🚀 Starting (unlimited)  
 **Total Planned**: 25+ chapters, 500+ hours of content  
-**Currently Available**: 7 chapters, 56 hours of content, 21 SVG diagrams
+**Currently Available**: 8 chapters, 64 hours of content, 24 SVG diagrams
 
 ---
 
@@ -21,7 +21,7 @@ Our vision for the future of AI education. This is a living document—prioritie
 ### Objectives
 - ✅ Establish master repository (DONE)
 - ✅ Complete Foundation Track (DONE)
-- ✅ Begin Practitioner Track (Ch 6-7 available)
+- ✅ Begin Practitioner Track (Ch 6-8 available)
 - 🔄 Establish community request process
 - 🔄 Build first 100 community chapters
 - ✅ Create core infrastructure and documentation (DONE)
@@ -37,11 +37,11 @@ Our vision for the future of AI education. This is a living document—prioritie
 - One new chapter released per week
 - New chapters unlock after reaching **10 newsletter subscribers**
 - ✅ Foundation Track complete (Chapters 1-5)
-- ✅ Practitioner Track started (Chapters 6-7)
+- ✅ Practitioner Track started (Chapters 6-8)
 
 ### Metrics to Track
 - Newsletter subscribers (target: 10 to unlock weekly releases)
-- Chapters completed: 7 / 25
+- Chapters completed: 8 / 25
 - Community requests received
 - Stars on master repo
 
@@ -59,7 +59,7 @@ Our vision for the future of AI education. This is a living document—prioritie
 ### Practitioner Track Chapters
 - [x] Chapter 6: Introduction to Machine Learning
 - [x] Chapter 7: Supervised Learning (Regression & Classification)
-- [ ] Chapter 8: Unsupervised Learning
+- [x] Chapter 8: Unsupervised Learning
 - [ ] Chapter 9: Deep Learning Fundamentals
 - [ ] Chapter 10: Natural Language Processing Basics
 - [ ] Chapter 11: Large Language Models & Transformers

diff --git a/SYLLABUS.md b/SYLLABUS.md
@@ -16,7 +16,7 @@ graph TD
 
     CH6["Ch 6: Intro to ML<br/>8h | Available"]
     CH7["Ch 7: Supervised Learning<br/>10h | Available"]
-    CH8["Ch 8: Unsupervised Learning<br/>8h | Coming Soon"]
+    CH8["Ch 8: Unsupervised Learning<br/>8h | Available"]
     CH9["Ch 9: Deep Learning<br/>12h | Coming Soon"]
     CH10["Ch 10: NLP Basics<br/>10h | Coming Soon"]
     CH11["Ch 11: LLMs & Transformers<br/>10h | Coming Soon"]
@@ -56,7 +56,7 @@ graph TD
     style CH5 fill:#4caf50,color:#fff
     style CH6 fill:#4caf50,color:#fff
     style CH7 fill:#4caf50,color:#fff
-    style CH8 fill:#f3e5f5
+    style CH8 fill:#4caf50,color:#fff
     style CH9 fill:#f3e5f5
     style CH10 fill:#f3e5f5
     style CH11 fill:#f3e5f5
@@ -66,7 +66,7 @@ graph TD
     style CH15 fill:#f3e5f5
 ```
 
-**Legend**: Green = Available | Purple = Practitioner (Coming Soon) | Chapters 1-7 fully available with SVG diagrams
+**Legend**: Green = Available | Purple = Practitioner (Coming Soon) | Chapters 1-8 fully available with SVG diagrams
 
 ---
 
@@ -81,7 +81,7 @@ graph TD
 | 5 | [Software Design & Best Practices](./chapters/chapter-05-software-design/) | Foundation | 6h | Available | 3 notebooks, scripts, 5 exercises, 3 SVGs |
 | 6 | [Introduction to Machine Learning](./chapters/chapter-06-intro-machine-learning/) | Practitioner | 8h | Available | 3 notebooks, scripts, 5 exercises, 3 SVGs |
 | 7 | [Supervised Learning](./chapters/chapter-07-supervised-learning/) | Practitioner | 10h | Available | 3 notebooks, scripts, 5 exercises, 3 SVGs |
-| 8 | Unsupervised Learning | Practitioner | 8h | Planned | - |
+| 8 | [Unsupervised Learning](./chapters/chapter-08-unsupervised-learning/) | Practitioner | 8h | Available | 3 notebooks, scripts, 5 exercises, 3 SVGs |
 | 9 | Deep Learning Fundamentals | Practitioner | 12h | Planned | - |
 | 10 | Natural Language Processing | Practitioner | 10h | Planned | - |
 | 11 | LLMs & Transformers | Practitioner | 10h | Planned | - |

diff --git a/chapters/chapter-08-unsupervised-learning/README.md b/chapters/chapter-08-unsupervised-learning/README.md
@@ -0,0 +1,61 @@
+# Chapter 8: Unsupervised Learning
+
+**Track**: Practitioner | **Time**: 8 hours | **Prerequisites**: Chapters 1-6
+
+---
+
+## Learning Objectives
+
+By the end of this chapter, you will be able to:
+
+- Understand the difference between supervised and unsupervised learning
+- Implement K-Means clustering from scratch using NumPy
+- Apply hierarchical (agglomerative) clustering and interpret dendrograms
+- Use DBSCAN for density-based clustering with automatic cluster count detection
+- Evaluate clusters with the silhouette score, inertia, and the elbow method
+- Apply Principal Component Analysis (PCA) for dimensionality reduction
+- Implement t-SNE for 2D visualization of high-dimensional data
+- Perform anomaly detection with Isolation Forest and statistical methods
+- Build a complete customer segmentation pipeline end-to-end
+
+---
+
+## Chapter Structure
+
+```
+chapter-08-unsupervised-learning/
+├── README.md
+├── requirements.txt
+├── notebooks/
+│   ├── 01_introduction.ipynb     # K-Means, evaluation metrics, elbow method
+│   ├── 02_intermediate.ipynb     # Hierarchical, DBSCAN, Gaussian Mixture Models
+│   └── 03_advanced.ipynb         # PCA, t-SNE, anomaly detection, customer segmentation capstone
+├── scripts/
+│   ├── unsupervised_toolkit.py   # KMeansScratch, PCA, plotting utilities
+│   └── utilities.py              # Helper functions
+├── exercises/
+│   ├── exercises.py              # 5 exercises
+│   └── solutions/
+│       └── solutions.py          # Complete solutions
+├── assets/diagrams/
+│   ├── clustering_algorithms.svg # K-Means, Hierarchical, DBSCAN comparison
+│   ├── dimensionality_reduction.svg # PCA and t-SNE visual
+│   └── anomaly_detection.svg     # Normal vs anomalous points
+├── datasets/
+│   ├── customers.csv             # Synthetic customer data (300+ rows)
+│   └── sensors.csv               # Synthetic sensor data with anomalies (200+ rows)
+```
+
+## Time Estimate
+
+| Section | Time |
+|---------|------|
+| Notebook 01: Introduction (Clustering Basics) | 2.5 hours |
+| Notebook 02: Intermediate (Advanced Clustering) | 2.5 hours |
+| Notebook 03: Advanced (Dimensionality Reduction & Capstone) | 3 hours |
+| Exercises | Included in notebooks |
+| **Total** | **8 hours** |
+
+---
+
+**Generated by Berta AI | Created by Luigi Pascal Rondanini**
diff --git a/chapters/chapter-08-unsupervised-learning/assets/diagrams/anomaly_detection.svg b/chapters/chapter-08-unsupervised-learning/assets/diagrams/anomaly_detection.svg