Anomaly detection course
This course provides comprehensive training on anomaly detection techniques for real-time streaming data, covering dataset design, feature engineering, various detection methods, and operational deployment strategies to achieve reliable, scalable detection systems.
from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
This Anomaly Detection Course teaches you how to set clear detection targets, create practical streaming data sets, and build strong real-time features for login and telemetry events. You will evaluate rule-based, statistical, time-series, and machine learning approaches, then discover how to roll out, watch, and adjust them using solid alerting, testing, and operations methods for dependable, low-noise detection on a large 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 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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Giulio CarloDigital Marketing Student
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