concept:feature-scaling
Feature Scaling
Feature Scaling in the context of production machine-learning systems.
Lessons
Formal explanations, examples, simulations, and checkpoints.
Linear, probabilistic, neighbor & margin models
Naive Bayes under sparse data
Naive Bayes turns sparse counts into additive evidence—quickly, smoothly, and with assumptions in plain sight.
Linear, probabilistic, neighbor & margin modelskNN, distance, and local decisions
In kNN, representation defines the neighborhood, and the neighborhood defines the decision.
Linear, probabilistic, neighbor & margin modelsSVMs, margins, and kernels
SVMs learn from the boundary’s hardest points; kernels change geometry, not scores into probabilities.
Courses & assignments
Dependency-authoritative learning units.
Casebook
Reported facts and course reconstructions.