AI for Telecom Networks: ML, GenAI & NWDAF in Operations

About this course

This course contains the use of artificial intelligence. There is no shortage of AI claims in telecom. What is harder to find is a clear account of what actually runs in networks today, what is still a pilot, and how to tell the difference. This course gives you that, using problems a network team already recognises.

By the end you can explain the core machine learning methods through real network cases, say where large language models genuinely help an operations team, describe how the 5G core's own analytics function works, and choose a first AI deployment that is safe to run.

What the 6 sections cover

The AI toolbox, in network terms

Machine learning through telecom cases

GenAI and LLMs in operationsNWDAF and the network's own data pipeline

From SON to closed-loop automation

Trust, governance and your first deployment

How it is taught26 video lessons, about 3.8 hours in total, most between five and nine minutes. Every lesson is built around one idea and one diagram that you watch being assembled, with the real terms attached once the picture makes sense. Short on-screen checks let you test yourself as you go; they are for your own practice, not a grade. The first section is free to preview.

Background: Recommended: 5G Foundation, or general familiarity with how a mobile network is built. No prior AI knowledge needed.

Who it suits: network operations and NOC engineers, planning and optimisation engineers, telecom managers evaluating AI projects, and data people moving into telecom.

This course is independent training. It is not affiliated with or endorsed by 3GPP, ETSI or any AI vendor. Names of organisations and specifications are used only to describe the technology.

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