Artificial Intelligence and Machine Learning for PSM


In the active field of media, Machine Learning (ML) and Natural Language Processing (NLP), underpinned by Large Language Models (LLMs), act as key catalysts for technological innovation. Their strength lies in their ability to operate at scale, and their impact is profound and far-reaching. To navigate this landscape, it's essential to have a detailed understanding of the underlying concepts, design processes, and potential of these technologies:

  • Data analytics: ML and LLM offer a unique ability to traverse vast data landscapes, identify anomalies such as fake news, and understand trends.
  • Content enrichment: ML, NLP, and LLM automate and refine the content creation process, from content enrichment to content annotation and generation.
  • Operational efficiency: ML techniques improve the operational backbone of broadcasting, including facial recognition, speaker identification, and gender identification.

Dive into the practical world of ML and NLP, with a particular focus on developing a robust fake news detector using the classic Machine Learning approach and Large Language Models like ChatGPT. You will gain insights into both the theory and application of Machine Learning in media, with hands-on sessions dedicated to Natural Language Processing and text analysis.

By the end of this masterclass, you will not only have the theoretical knowledge but also the practical understanding to implement ML and LLM-driven solutions in the dynamic world of media. Join us and be at the forefront of the future of media technology!

 Who it's for

  •  Project Manager
  • Team Manager 
  • Product Owner
  • Engineer

 In this course, you will learn the theory and design steps of machine learning by designing a fake news detector. To take this course, you should have a general knowledge of a programming language, however, you don't need to be an expert in Python.

Skills learnt

  • Master the basic concepts of Machine learning, from understanding the training and testing phases to evaluating models using different metrics and overcoming challenges such as overfitting.
  • Immerse yourself in the comprehensive design process of Machine Learning projects and understand the entire development pipeline. You will design a Fake News detector.
  • Understand how to effectively use Large Language Models in media, with a particular focus on creating accurate prompts. You will use LLMs to analyze articles and explain why they are considered Fake.


 Day 1: from 9.00 to 17.00

 Day 2: from 9.00 to 17.00

 Programme Outline 

Module 1: Data handling 

  • ML Basics: A quick dive into core concepts: training, testing, metrics, and overfitting.
  • Data Handling: Analyse and clean news datasets, ensuring consistency and reliability.
  • Feature Extraction: Generate features in news articles that will feed Machine Learning tools.
  • Pipeline Creation: Set up an efficient process tailored for fake news detection.

Module 2: Building and Tuning AI Models

  • Training: Tune algorithms to optimize the scores in detecting text-based fake news.
  • Evaluation: Gauge model performance using specific metrics.
  • Model Selection: Identify the best-suited model.

Module 3: Advanced Insights with LLMs

  • LLM Overview: Understand the role of Large Language Models in media.
  • Prompting and Analysis with LLMs: Understand prompting strategies.
  • Hands-on Exercises: Create prompts to analyse articles and identify clues to determine if an article is fake.

 Equipment needed by participants

 Personal computer.


After completing the 3 modules, you will be awarded an EBU Academy Certificate