Regularised Linear and Logistic Models
This module helps learners deepen their use of linear models beyond the foundation baseline.
Browser lab plus download fallbackRead the lessons, open the module notebook in the browser lab when available, and keep the downloadable pack for a local copy. Write the algorithm decision evidence before chasing scores.
Notebook options
Use the browser lab for quick practice, or extract the ZIP and open modules/module-02/notebooks/module-02-algorithm-notebook.ipynb locally. The downloadable pack remains the fallback if browser storage, network, or device limits interrupt the lab.
Module sequence
- Linear models as strong baselinesUnit 1 · 25-40 minutes
- Scaling and coefficient interpretationUnit 2 · 25-40 minutes
- Ridge and L2 regularisationUnit 3 · 25-40 minutes
- Lasso and feature selection behaviourUnit 4 · 25-40 minutes
- Elastic Net and correlated featuresUnit 5 · 25-40 minutes
- Polynomial and interaction features with cautionUnit 6 · 25-40 minutes
- Logistic regression regularisation and class weightsUnit 7 · 25-40 minutes
- Project step: regularised model comparisonUnit 8 · 25-40 minutes
- Activity: Create a regularised model comparison notePractice activity · 45-75 minutes
- Module 2 Assessment: Regularised Linear and Logistic Models CheckModule check · 30-45 minutes