This course provides a practical, hands-on introduction to Large Language Models (LLMs), focusing on how modern AI systems understand, process, and generate human language. It is structured into progressive chapters that build foundational knowledge step by step.
The course begins with an introduction to LLMs, explaining what large language models are, how they work at a high level, and why they are central to modern artificial intelligence applications.
Learners then explore tokens and embeddings, which are core concepts for representing text in a format that machine learning models can process. These representations form the basis of how LLMs understand language.
A deeper section examines what happens inside large language models, helping learners understand internal model behavior and how transformer-based systems process input data.
The course continues with practical applications such as text classification, where LLMs are used to categorize and organize textual data, and text clustering and topic modeling, which help discover hidden patterns in large datasets.
Prompt engineering is also covered, teaching learners how to design effective prompts to guide LLM behavior and improve output quality.
Advanced chapters introduce text generation techniques, showing how models produce coherent and context-aware text outputs.
Finally, the course explores semantic search and Retrieval-Augmented Generation (RAG), where LLMs are combined with external knowledge sou
