anomaly detection course
This course provides hands-on training in anomaly detection for real-time systems, covering dataset creation, feature engineering, model evaluation, and deployment strategies for scalable, reliable detection.

4 to 360 hours of flexible workload
certificate valid in your country
What Will I Learn?
This Anomaly Detection Course teaches you how to set clear detection objectives, create practical streaming datasets, and develop effective real-time features for login and telemetry events. You will evaluate rule-based, statistical, time-series, and machine learning models, then discover how to deploy, monitor, and optimise them using solid alerting, testing, and operational methods for dependable, low-noise detection at scale.
Elevify Advantages
Develop Skills
- Design streaming anomaly datasets: simulate attacks and realistic login events.
- Engineer real-time features: velocity, seasonality and per-user risk signals.
- Apply ML and rules: isolation forest, ARIMA, thresholds for fast anomalies.
- Build low-latency pipelines: Kafka, Flink, model APIs and alert routing.
- Tune, test and monitor detectors: replay traffic, metrics, drift and runbooks.
Suggested Summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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