Pluralsight - Boosting Techniques

This course will teach you how to implement, optimize, and interpret boosting algorithms like XGBoost, LightGBM, and CatBoost to enhance predictive performance in real-world applications.
Boosting techniques are powerful methods for improving machine learning model accuracy by combining weak learners into strong predictors. Building accurate machine learning models can be challenging when dealing with bias, variance, and feature interactions, making it essential to use advanced ensemble techniques for improved predictive performance. In this course, Boosting Techniques, you'll learn to implement, optimize, and interpret powerful boosting algorithms such as XGBoost, LightGBM, and CatBoost. First, you'll explore the fundamental concepts of ensemble learning, comparing boosting with other techniques like bagging and stacking. Next, you'll discover how to build and evaluate boosting models for classification and regression tasks, leveraging GPU acceleration and handling categorical data effectively. Finally, you'll learn how to fine-tune hyperparameters and use SHAP values to interpret model predictions and assess feature importance. When you're finished with this course, you'll have the skills and knowledge of boosting techniques needed to build high-performing models that enhance predictive accuracy and drive data-driven decision-making.
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