Support Vector Machines
This module helps learners use SVMs as practical tools with clear boundaries.
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-05/notebooks/module-05-algorithm-notebook.ipynb locally. The downloadable pack remains the fallback if browser storage, network, or device limits interrupt the lab.
Module sequence
- Margins and support vectors in plain languageUnit 1 · 25-40 minutes
- Linear SVMs for classificationUnit 2 · 25-40 minutes
- Support vector regression at a practical levelUnit 3 · 25-40 minutes
- Kernels: linear, polynomial, and RBF intuitionUnit 4 · 25-40 minutes
- Scaling and hyperparameters C and gammaUnit 5 · 25-40 minutes
- Runtime and interpretability trade-offsUnit 6 · 25-40 minutes
- Project step: SVM candidate reviewUnit 7 · 25-40 minutes
- Activity: Create an SVM candidate reviewPractice activity · 45-75 minutes
- Module 5 Assessment: Support Vector Machines CheckModule check · 30-45 minutes