Anomaly detection course
This course equips you to excel in anomaly detection for real-time systems. You will learn to craft streaming datasets, develop potent features, evaluate machine learning and rule-based models, and construct complete detection pipelines that reduce fraud, thwart attacks, and secure vital technology platforms effectively.
flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
Gain expertise in defining clear anomaly detection objectives, creating lifelike streaming data sets, and building effective real-time features from login and telemetry data. Compare rule-based, statistical, time-series, and machine learning approaches, and master deployment, monitoring, and optimisation using strong alerting, testing, and operational strategies for scalable, low-noise detection.
Elevify advantages
Develop skills
- Design streaming anomaly datasets by simulating attacks and realistic login events.
- Engineer real-time features including velocity, seasonality, and per-user risk signals.
- Apply machine learning and rule-based methods like isolation forest, ARIMA, and thresholds for rapid anomaly spotting.
- Build low-latency pipelines using Kafka, Flink, model APIs, and alert routing mechanisms.
- Optimise, test, and monitor detectors with traffic replay, metrics tracking, drift detection, 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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