XGBoost and LightGBM for High-Performance Models focus on advanced gradient boosting algorithms used for accurate and efficient machine learning predictions. These frameworks are widely used for structured data problems because they deliver fast training speeds and strong predictive performance. This training explains how boosting techniques combine multiple decision trees to improve model accuracy and reduce errors. It also covers feature selection, hyperparameter tuning, regularization, and model evaluation methods. You will learn how XGBoost and LightGBM support applications such as classification, regression, and ranking tasks. The course also highlights best practices for building scalable, optimized, and high-performance machine learning models.
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