This course, Practical Introduction to Large Language Models (LLMs) – Full Series, provides hands-on guidance for working with large language models in real-world scenarios. Starting with a practical overview of LLMs, the series covers the OpenAI Python API, Hugging Face Transformers library, and building chatbot UIs with Gradio.

Students will explore prompt engineering techniques, fine-tuning LLMs with example code, and building models from scratch. The course also demonstrates how to create custom AI assistants using RAG, external tools, and fine-tuning approaches, ensuring LLMs perform effectively for specific tasks.

Advanced topics include QLoRA for fine-tuning on a single GPU, improving LLMs with RAG, and leveraging text embeddings, classification, and semantic search with Python. Each lecture provides both theoretical understanding and practical coding demonstrations, equipping learners with the skills to implement, optimize, and innovate with LLMs.

By the end of this series, learners will be capable of building, fine-tuning, and deploying LLM-based AI solutions for business, research, and development, combining deep learning principles with practical AI programming techniques.