3231 - Imbalanced Data & Anomaly Detection - 2020

Issued by GAIn® - The Global AI network

We often face the problem of looking for a needle in a haystack of data. This two-day badge will equip you with two tools to do so. One is aimed at working with imbalanced datasets in supervised learning, for instance by creating many more needles so they are easier to find. The other will help you to identify what a needle is in the first place, using unsupervised anomaly detection methods. Concluding with an analytical task applying these skills to detect anomalies in a real-life case.

Skills

Earning Criteria

Introduction to anomaly detection: Able to explain the context of anomalies and distinguish between different types.
Graphical anomaly detection: Able to detect anomalies using data plotting.
Handling imbalanced datasets: Able to maximize model results from imbalanced datasets.
Unsupervised anomaly detection: Knowing when unsupervised learning can be applied and how to evaluate an unsupervised model.
Time-series anomaly detection: Able to detect anomalies in time-series data.
Unsupervised algorithms: Able to apply two unsupervised algorithms to detect anomalies.
Detecting fraud using all anomaly detection tools: Able to combine all your knowledge to detect anomalies in a real-life case.
Exam.