Learning objectives
- • Compare margin-based, distance-based, and probabilistic classifiers
- • Explain kernel intuition and why scaling matters for SVM and KNN
- • Choose fast baselines for text, tabular, and sparse-feature problems
Traditional ML
SVM, KNN, Naive Bayes, and Classic Baselines
Know the classic algorithms interviewers still use to test assumptions, distance metrics, kernels, and baseline thinking.
Recommended on day 1780 minutesIntermediate
Interview prompts
- • When would Naive Bayes beat a more flexible model?
- • Why is KNN sensitive to feature scaling and high-dimensional data?
Prerequisites