Tree-based methods,
in this course we will learn about Tree-Based Methods course, a fundamental topic in machine learning that focuses on decision tree structures for classification and regression tasks. You will explore how decision trees split data using features, how to prune trees for better generalization, and how ensemble methods like Random Forest and Gradient Boosting improve predictive accuracy. This course covers key algorithms such as CART, Random Forest, XGBoost, and LightGBM. You'll also learn about feature importance, hyperparameter tuning, and how to interpret tree-based models. Through hands-on projects and real-world examples, you'll gain practical experience in implementing and evaluating tree-based models using Python libraries like scikit-learn and XGBoost. Whether you're a beginner or an intermediate learner, this course will build a strong foundation in decision-tree algorithms for solving complex data problems. Data School
