Learning Path

Anomaly Detection and Rare Event Identification with Machine Learning

Machine Learning Fundamentals → Practical Applications and Case Studies

18 Knowledge Nodes · 18 Learning Steps · v0 Active

This advanced graduate-level path equips ML practitioners with the knowledge and skills to identify outliers and rare events using machine learning. It covers statistical foundations, classic algorithms like Isolation Forest and One-Class SVM, deep learning approaches such as autoencoders, and appropriate evaluation metrics for imbalanced data.

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Learning Route

18 learning steps · 4 phases. Click any step to inspect it and see it on the Knowledge Map.

Phase 1

Foundation

5 steps

Phase 2

Core Concepts

5 steps

Phase 3

Advanced Topics

4 steps

Phase 4

Specialization

4 steps