Add Random Forest & Ensemble Learning interactive lesson

[LMK89]

Summary

Added a comprehensive interactive educational lesson on Random Forest and Ensemble Learning techniques in Vietnamese, complete with interactive demos, quizzes, and warmup exercises.

Key Changes

• New lesson file: 2026-08-26-random-forest-ensemble-35f36b72.html — A complete standalone HTML lesson covering:

  • Ensemble Learning fundamentals and definitions
  • Real-world customer churn prediction use case
  • 4 comparative states: Single Decision Tree, Bagging/Random Forest, Boosting, and Stacking
  • Comprehensive comparison table with accuracy metrics and trade-offs
  • Interactive demo: 5-tree voting system where users input customer data and see individual tree predictions
  • Good vs. bad data practices section
  • Practice exercises and self-grading quiz with 3 questions
  • Warmup section with fill-in-the-blank, multiple choice, and matching exercises

• Updated README.md: Added entry for the new lesson in the course index table

Implementation Details

• Responsive design: Mobile-first CSS with dark/light theme toggle using CSS custom properties
• Interactive features:
  • Theme persistence via localStorage
  • Real-time demo with animated tree voting visualization
  • Self-checking warmup and quiz with immediate feedback
  • Random data generator for demo exploration
• Educational structure: Follows progressive learning pattern (warmup → definitions → real-world problem → 4 states → demo → practice → quiz)
• Accessibility: Semantic HTML, clear visual hierarchy, emoji indicators for quick scanning
• Performance: Single-file design with embedded CSS and vanilla JavaScript (no external dependencies)

https://claude.ai/code/session_013qJsSm1vrA9oWXXY1xmi2n