Overview
Curriculum
What if our linear models could stay accurate and simple, even in messy real-world data? In this module, we will build models that balance precision and simplicity. We will learn how to control model complexity, use regularization to prevent overfitting, and tune hyperparameters so our models make reliable, real-world predictions.
What You'll Learn
- Balancing model complexity to avoid underfitting and overfitting
- Applying L1 (Lasso) and L2 (Ridge) regularization to control complexity
- Tuning key hyperparameters for optimal performance
- Evaluating model fit and use cross-validation to improve generalization
- Interpreting the bias-variance trade-off when tuning linear models
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