Model Tuning with GridSearchCV and Nested Cross-Validation

Selecting optimal hyperparameters without overfitting is essential for building reliable machine learning workflows. This written course provides a clear foundation in model optimization, guiding you through hyperparameter tuning and unbiased performance evaluation using nested cross-validation. What you will learn: Understand the fundamentals of cross-validation strategies and hyperparameter spaces. Implement GridSearchCV to systematically evaluate model configurations. Apply nested cross-validation routines to prevent data leakage and evaluate true generalization error. Build robust preprocessing and modeling workflows using scikit-learn pipelines. Practice tuning both classification and regression algorithms with modern Python patterns. The curriculum begins with core terminology and evaluation principles before moving into practical code walkthroughs and structured exercises. This guide is crafted for beginner data practitioners and developers looking to master model evaluation best practices. Begin reading to strengthen your machine learning evaluation techniques.