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

    Use linear regression as a production baseline to move the classical baselines production brief toward a defensible release.

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

    Logistic regression, odds, and calibration

    Use logistic regression, odds, and calibration to move the classical baselines production brief toward a defensible release.

    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

    Use naive bayes under sparse data to move the classical baselines production brief toward a defensible release.

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

    kNN, distance, and local decisions

    Use knn, distance, and local decisions to move the classical baselines production brief toward a defensible release.

    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

    Use svms, margins, and kernels to move the classical baselines production brief toward a defensible release.

    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