Distance-Based Methods and Kernel Intuition
This module helps learners understand when distance is meaningful.
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-03/notebooks/module-03-algorithm-notebook.ipynb locally. The downloadable pack remains the fallback if browser storage, network, or device limits interrupt the lab.
Module sequence
- What distance means in feature spaceUnit 1 · 25-40 minutes
- k-nearest neighbours classification and regressionUnit 2 · 25-40 minutes
- Scaling, irrelevant features, and dimensionalityUnit 3 · 25-40 minutes
- Choosing k and weighting neighboursUnit 4 · 25-40 minutes
- Kernel intuition without heavy mathematicsUnit 5 · 25-40 minutes
- When distance methods become slow or fragileUnit 6 · 25-40 minutes
- Project step: distance-sensitive model reportUnit 7 · 25-40 minutes
- Activity: Create a distance-sensitive model reportPractice activity · 45-75 minutes
- Module 3 Assessment: Distance-Based Methods and Kernel Intuition CheckModule check · 30-45 minutes