University of Glasgow via Coursera
Large Language Models (LLMs), including GPT models that power applications such as ChatGPT, are transforming how people interact with technology and how computers understand and generate language. In this course, you'll explore the core concepts of natural language processing (NLP) and language modelling that underpin today's generative AI systems.
You'll learn how language models are trained, how Transformer architectures revolutionised modern AI, and why they have become the foundation for a wide range of applications, from conversational assistants and content generation to summarisation, translation, and question answering. Along the way, you'll examine the strengths and limitations of LLMs, including topics such as hallucinations, evaluation, responsible AI, and the ethical considerations involved in developing and deploying these technologies.
Through hands-on Python labs, you'll explore the building blocks of Transformer-based language models, experiment with text generation, and gain practical experience applying smaller language models to real-world tasks. Regular practice quizzes and interactive learning activities will reinforce key concepts and help prepare you for the graded assessments.
Whether you're looking to understand how modern LLMs work or build a foundation for working with generative AI, this course provides the knowledge and practical experience to get started.
This module introduces the concept of language modelling, which is the foundation of models like GPT.
This module describes the technical background for neural language models and an overview of how they are used to generate text.
This module examines key considerations for using GPT and similar models in real‑world contexts, focusing on their limitations, associated risks, and approaches to managing those risks in practice.
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University of Glasgow is in Tier 3: good universities, respected companies, nonprofits and well-known teachers of our institution ranking (74/100).
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