Lilly Tech Systems
XGBoost & LightGBM Mastery
Master the gradient boosting frameworks that dominate Kaggle competitions and power production ML systems. Learn XGBoost, LightGBM, CatBoost, and advanced tuning techniques.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Gradient boosting fundamentals, decision trees, bias-variance tradeoff, and ensemble learning.
2. XGBoost
XGBoost API, parameters, regularization, handling missing values, and feature importance.
3. LightGBM
Histogram-based splitting, leaf-wise growth, categorical feature handling, and GPU training.
4. CatBoost
Ordered boosting, native categorical support, symmetric trees, and ranking tasks.
5. Tuning
Bayesian optimization, Optuna, early stopping, learning rate schedules, and competition strategies.
6. Best Practices
Production deployment, model interpretation, SHAP values, and scaling to large datasets.
What You'll Learn
By the end of this course, you'll be able to:
Master Gradient Boosting
Understand the theory behind gradient boosting and when to use it over other approaches.
Choose the Right Framework
Select between XGBoost, LightGBM, and CatBoost based on your data and requirements.
Tune Like a Pro
Use Bayesian optimization and advanced strategies to achieve competition-winning performance.
Interpret Models
Explain predictions with SHAP values and feature importance for business stakeholders.