Phase 3: Production ML Practices · ~75 minutes · Python

Anomaly Detection

Normal is easy to define. Abnormal is whatever doesn't fit.

What you will learn

Implement Z-score, IQR, and Isolation Forest anomaly detection methods from scratch
Distinguish between point, contextual, and collective anomalies and select the appropriate detection method for each
Explain why anomaly detection is framed as modeling normal data rather than classifying anomalies
Compare unsupervised anomaly detection with supervised classification and evaluate the tradeoff between novel anomaly coverage and precision

Introduction

Type: Build
Language: Python
Prerequisites: Earlier lessons in this course
Time: ~75 minutes

Objective

Learning objectives

Implement Z-score, IQR, and Isolation Forest anomaly detection methods from scratch
Distinguish between point, contextual, and collective anomalies and select the appropriate detection method for each
Explain why anomaly detection is framed as modeling normal data rather than classifying anomalies
Compare unsupervised anomaly detection with supervised classification and evaluate the tradeoff between novel anomaly coverage and precision

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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