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Module · 6 lessons
Model Optimization & Feature Engineering
Squeeze the best generalization from your models with feature engineering, regularization, and cross-validation.
At a glance
Level
Intermediate
Lessons
6 lessons
Time to complete
2 weeks
Cost
Free forever · no sign-up
Great models come from great features and rigorous evaluation. This module covers the craft of preparing data, selecting models, applying regularization to fight overfitting, and validating performance so your models generalize to new data.
Lessons in this module
1
Feature Engineering
Learn feature engineering for machine learning by encoding categoricals, building ratio features, and taming skew on the real California Housing dataset
2
Model Selection
Compare five model families on the same dataset with one shared interface and learn how to choose the right model for the job
3
Cross-Validation
Learn why a single train/test split is noisy and how k-fold cross-validation gives a more honest, lower-variance estimate of model performance
4
Regularization
Fight overfitting in linear models with Ridge and Lasso regularization using the real California Housing dataset
5
Going Beyond Linear Models
Capture nonlinear patterns with polynomial features and tree-based models, and learn to navigate the bias-variance trade-off on the California Housing dataset
6
Guided Project: Optimizing Model Prediction
Build an end-to-end regression project on the Forest Fires dataset and learn why a near-zero R2 can be an honest, valuable result
Achievement
Complete all 6 lessons to finish the Model Optimization & Feature Engineering module.