Prerequisites: Strong foundation in machine learning concepts, including supervised learning, decision trees, ensemble methods, and calculus/linear algebra fundamentals. Proficiency in Python and common ML libraries (Scikit-learn, Pandas, NumPy).

Level: Advanced

What You'll Learn

• Gradient Boosting Theory

  Understand the mathematical foundations and algorithmic details of Gradient Boosting Machines (GBMs).

• Advanced Algorithm Implementation

  Implement and configure XGBoost, LightGBM, and CatBoost using their respective libraries.

• Regularization Techniques

  Apply advanced regularization methods specifically designed for boosting algorithms to prevent overfitting.

• Hyperparameter Optimization

  Employ sophisticated strategies for tuning hyperparameters of gradient boosting models for optimal performance.

• Model Interpretation

  Utilize techniques like SHAP to interpret the predictions and behavior of complex gradient boosting models.

• Performance Optimization

  Optimize gradient boosting models for speed, memory usage, and predictive accuracy on large datasets.

• Customization

  Implement custom loss functions and evaluation metrics within boosting frameworks.