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- Header with learning objectives and time estimate - Supervised vs unsupervised learning comparison with make_blobs visualization - K-Means algorithm theory (initialize, assign, update) - K-Means from scratch implementation (KMeansScratch class) - Step-by-step iteration visualization (2x2 subplot grid) - Cluster evaluation: inertia and silhouette score with silhouette plot - Elbow method for choosing K (inertia + silhouette vs K) - Scikit-learn KMeans comparison with scratch implementation - Practical tips: assumptions, scaling, n_init, failure modes - Summary and key takeaways Co-authored-by: Luigi Pascal Rondanini <luigi@rondanini.com>
Rich educational notebook covering: - PCA theory and from-scratch NumPy implementation - PCA with scikit-learn (comparison with scratch version) - t-SNE for non-linear 2D visualization with perplexity analysis - Anomaly detection: Z-Score and Isolation Forest - Full customer segmentation capstone project: synthetic data generation, scaling, PCA, elbow method, K-Means clustering, segment profiling, and business recommendations 42 cells (23 code, 19 markdown), ~3 hours of content. Co-authored-by: Luigi Pascal Rondanini <luigi@rondanini.com>
Covers hierarchical (agglomerative) clustering with dendrograms, DBSCAN for density-based clustering with parameter selection, Gaussian Mixture Models with probability contours and BIC/AIC, and a comprehensive algorithm comparison across data geometries. Co-authored-by: Luigi Pascal Rondanini <luigi@rondanini.com>
- 3 notebooks: clustering basics, advanced clustering, dimensionality reduction & capstone - KMeansScratch and PCAScratch implementations in unsupervised_toolkit.py - 5 exercises with complete solutions - 3 SVG diagrams: clustering algorithms, dimensionality reduction, anomaly detection - 2 synthetic datasets: customers.csv (300 rows), sensors.csv (200 rows) - Docs: chapter overview, 3 content pages for online reading - Updated mkdocs.yml navigation, homepage stats (8 chapters, 64h, 24 SVGs) - Updated curriculum, syllabus, roadmap, README, and all status references Co-authored-by: Luigi Pascal Rondanini <luigi@rondanini.com>
- Updated welcome email to highlight Chapter 8: Unsupervised Learning - New send-chapter-notification function to email all subscribers about new chapter releases via Netlify Forms API + Resend - Supports chapter_number, chapter_title, chapter_description params - Paginates through all form submissions to find subscriber emails Co-authored-by: Luigi Pascal Rondanini <luigi@rondanini.com>
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- New deploy-succeeded.js fires automatically after each Netlify deploy - Reads NOTIFY_CHAPTER env var to determine if a notification should be sent - Fetches all subscribers from Netlify Forms API, sends via Resend - Set NOTIFY_CHAPTER=8 and NOTIFY_TITLE='Unsupervised Learning' in Netlify env vars, then deploy to send the Chapter 8 newsletter - Remove NOTIFY_CHAPTER after sending to prevent re-sends - Updated netlify.toml with functions directory and env var documentation Co-authored-by: Luigi Pascal Rondanini <luigi@rondanini.com>
Co-authored-by: Luigi Pascal Rondanini <luigi@rondanini.com>
- Newsletter is now driven by chapter_notification.json in the repo - deploy-succeeded.js reads the JSON, compares chapter number against LAST_NOTIFIED_CHAPTER env var to prevent re-sending - After sending, it updates LAST_NOTIFIED_CHAPTER via Netlify API - Workflow: edit chapter_notification.json, commit, deploy — done - Removed send-chapter-notification.js (replaced by deploy-succeeded) - Simplified netlify.toml documentation Co-authored-by: Luigi Pascal Rondanini <luigi@rondanini.com>
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Introduce Chapter 8: Unsupervised Learning and implement a new chapter notification system for subscribers.