How do you know a machine learning model will actually work on new data? This module covers the tools for honest model evaluation: train-test splits, validation sets, cross-validation, metrics, and the common traps that make scores look better than they really are.

Start here:

• Model Evaluation - A Friendly Guide — The complete, beginner-friendly guide to evaluating models without fooling yourself.
• Hyperparameters and Tuning - A Friendly Guide — How to choose the settings that control how a model learns.
• Model Building and Validation - Telco Churn Example — A hands-on walkthrough of data cleaning, pipelines, holdout, and cross-validation on an imbalanced churn dataset.

What You’ll Learn:

• Why training score is not enough and what generalisation means
• How to split data into training, validation, and test sets
• When and why to use cross-validation
• Which metrics to use for classification and regression
• How to search for good hyperparameters
• How to avoid data leakage and overfitting the test set