5G Core & IMS Training

Turn the telemetry every earlier course emits into a full data-science lifecycle — ingestion, a time-aligned feature store, analytics on standardized TS 28.554 KPIs, visualization for two audiences, and machine learning (Isolation-Forest anomaly detection, DTW fault classification, seasonal forecasting). The recurring lesson is honesty: a 0.99 score is usually a leak, and every model ends by asking what would be true on real traffic. Ships two notebooks — a synthetic edition that runs anywhere, and a live-lab edition on real telemetry.

You leave able to: You can build an honest pipeline from packets to predictions — and you trust the lower, leak-free number over the flattering one.

The honest case for deep learning in a telco: when a deep, graph or federated method genuinely beats the classical baseline from Course 05 — and, just as often, when it does not. CNNs/ROCKET for KPI time series, sequence models and self-attention for signalling ladders, graph neural nets that localize a fault on the service-based topology itself, deep anomaly detection, federated learning across sites that never share raw data, and a trust layer — conformal prediction, drift detection and a human in the loop. Offline, no GPU required.

You leave able to: You can pick the right model for a telco problem on evidence, and wrap it in the calibration and drift checks that make it safe to trust.