Learn/Course 04
Classical ML · weeks 6

Linear, probabilistic, neighbor & margin models

Support tickets need explainable routing under a 15 ms latency budget.

Opening production brief

A measurable problem, before a model.

Users

Operators, product owners, and people affected by the classical baselines decision.

Data

regularization, log odds, Bayes rule, feature scaling with version, owner, event time, and availability contracts.

Constraints

Support tickets need explainable routing under a 15 ms latency budget. Every solution must state latency, cost, capacity, and fallback limits.

Success

Offline evidence plus a deployable resolution: A justified baseline across regression, Naive Bayes, kNN, and SVM.

CHAPTER 1

Frame and baseline

Turn the production problem into explicit data, metric, and baseline contracts.

  1. 01

    Linear regression as a production baseline

    A linear baseline is valuable because its assumptions, errors, and serving cost are easy to see.

    65–90 min · checkpoint · deterministic lab
  2. 02

    Logistic regression, odds, and calibration

    Logistic regression adds evidence in log-odds space; calibration and policy give that score operational meaning.

    65–90 min · checkpoint · deterministic lab
CHAPTER 2

Build and stress

Formalize the machinery, test counterexamples, and expose system limits.

  1. 03

    Naive Bayes under sparse data

    Naive Bayes turns sparse counts into additive evidence—quickly, smoothly, and with assumptions in plain sight.

    65–90 min · checkpoint · deterministic lab
  2. 04

    kNN, distance, and local decisions

    In kNN, representation defines the neighborhood, and the neighborhood defines the decision.

    65–90 min · checkpoint · deterministic lab
CHAPTER 3

Resolve and operate

Join model behavior to architecture, observability, rollout, and rollback.

  1. 05

    SVMs, margins, and kernels

    SVMs learn from the boundary’s hardest points; kernels change geometry, not scores into probabilities.

    65–90 min · checkpoint · deterministic lab
  2. HW

    Explainable ticket-routing baseline

    Compare linear/logistic regression, Naive Bayes, kNN, and SVM under one evaluation and serving contract.

    Rubric · staged hints · reference resolution
Prerequisite guidance

2 concepts make this faster.

Prerequisites are guidance, never hard gates.

C02EvaluationA metric contract, leakage-safe validation, calibration analysis, and experiment plan.C03Data & featuresData contracts, point-in-time features, backfills, quality gates, and lineage.
Ready to work the problem?

Linear regression as a production baseline

Start lesson 01