This hands-on course provides a practical introduction to Large Language Models (LLMs), focusing on real-world applications and core AI concepts. It is designed to help learners understand how LLMs work internally while also applying them in practical tasks such as text processing, generation, and retrieval systems.
The course begins with an introduction to LLM fundamentals, including how language models function and how text is processed into tokens and embeddings. You will learn how tokenization and vector representations form the foundation of modern AI systems.
Next, the course explores key machine learning tasks such as text classification and topic modeling, helping learners understand how AI can organize and interpret large volumes of text data.
The course also covers prompt engineering techniques, showing how to design effective prompts to improve AI responses. Advanced topics include text generation strategies, semantic search, and Retrieval-Augmented Generation (RAG), which enhances AI accuracy by connecting it to external knowledge sources.
In addition, learners will explore multimodal LLMs, which combine text with other data types, and learn how to build embedding models for semantic understanding. The course also includes fine-tuning techniques for both classification and generative models.
By the end of this course, learners will have a complete practical understanding of LLM workflows, from tokenization and embeddings to RAG systems, fine-tuning, and advanced AI applications.
