XGBoost for Regression and Model Evaluation

Accurately predicting continuous outcomes is an essential skill in applied machine learning and data science. This text-based course guides you through building, tuning, and evaluating regression models using XGBoost, the leading framework for tabular data. You will move from essential terminology to hands-on model evaluation, learning how to measure accuracy, prevent overfitting, and fine-tune model parameters for reliable real-world performance. What you'll learn: - Understand the core concepts and principles behind gradient boosting and regression trees - Prepare and preprocess tabular datasets for training XGBoost regressors - Evaluate model performance using standard metrics including RMSE, MAE, and R-squared - Apply cross-validation techniques to ensure stable generalization on unseen data - Tune key hyperparameters such as learning rate, tree depth, and regularization - Analyze feature importance to understand predictive drivers in your models The course begins with foundational machine learning definitions and structural concepts before walking through structured written explanations, code implementations, and practical evaluation workflows. This course is created for data analysts, aspiring data scientists, and developers looking for a clear, step-by-step guide to regression with XGBoost. No prior experience with advanced machine learning is required. Start reading today to build and validate accurate regression models with confidence.