0. Why CatBoost?

1. Basic Machine Learning Concepts

• Bias and Variance
• Basic Decision Tree: Classification, Regression, Feature Selection, Handling Missing Data

2. Data Encoding Techniques

• One-Hot Encoding
• Label Encoding
• Target Encoding
• K-Fold Target Encoding

3. Ensemble Methods

• Random Forest
• AdaBoost
• Gradient Boosting: Regression, Classification

4. Regularization Techniques

• L2 Regression (Ridge)
• Lasso (L1) Regression
• Elastic Net Regression

5. Advanced Boosting Algorithms

• XGBoost
• CatBoost

6. Model Evaluation Metrics

• Accuracy
• Precision
• Recall
• F1-Score
• ROC-AUC

7. Practical Applications

• Portfolio Construction
• Portfolio Management
• Risk Assessment
• Performance Optimization