Understanding ML Model Relationships
Interactive guide to understanding how different ML techniques connect, from basic models through regularization to ensemble methods like Random Forest and XGBoost
🗺️ Chapter 1: The ML Landscape Map
Visual Hierarchy & Cooking Metaphor
- Interactive ML technique hierarchy
- Cooking metaphor for understanding relationships
- Connection web visualization
- Foundation for understanding complexity
Foundation
Visual
Interactive
🔧 Chapter 2: Foundation Models & Problems
Linear Regression & Decision Trees
- Linear regression bias-variance tradeoff
- Decision tree overfitting demonstration
- Interactive parameter adjustment
- Understanding model limitations
Basic Models
Overfitting
Interactive
⚖️ Chapter 3: Regularization - Problem Solvers
L1 vs L2 Regularization Deep Dive
- L1 vs L2 interactive comparison
- Feature selection visualization
- Regularization path exploration
- When to use each technique
Regularization
Feature Selection
Advanced
🤝 Chapter 4: Ensemble Methods - Power of Many
Bagging vs Boosting Concepts
- Ensemble voting mechanism simulation
- Bagging vs boosting animated comparison
- Wisdom of crowds demonstration
- Ensemble size vs performance analysis
Ensemble
Bagging
Boosting
🌳 Chapter 5: Random Forest Deep Dive
Interactive Forest Builder
- Build random forest step-by-step
- Bootstrap sampling visualization
- Feature importance exploration
- Out-of-bag error tracking
Random Forest
Bootstrap
Interactive
📈 Chapter 6: Gradient Boosting Mastery
Sequential Learning Visualization
- Sequential model training animation
- Residual learning visualization
- Learning rate impact demonstration
- Loss function progression tracking
Gradient Boosting
Sequential
Advanced
🏆 Chapter 7: XGBoost - The Champion
Performance Optimization Playground
- XGBoost vs other methods comparison
- Hyperparameter tuning playground
- Performance benchmarking dashboard
- Real dataset application demo
XGBoost
Optimization
Practical
🎯 Chapter 8: Putting It All Together
Algorithm Selection & Integration
- Algorithm selection decision tree
- Performance comparison dashboard
- Interactive case study walkthroughs
- Real-world application guidelines
Decision Making
Integration
Practical
🧭 Course Navigation
Prerequisites:
• Basic understanding of machine learning concepts
• Familiarity with supervised learning (regression/classification)
• High school mathematics (algebra, basic statistics)
• Curiosity about understanding ML algorithm relationships!