Fine-Tuning & Optimizing Large Language Models

This course provides a comprehensive, hands-on journey into model adaptation, fine-tuning, and context engineering for large language models (LLMs). It focuses on how pretrained models can be efficiently customized, optimized, and deployed to solve real-world NLP problems across diverse domains. Through structured lessons, demonstrations, and practice assignments, you will learn how to apply transfer learning, parameter-efficient fine-tuning techniques, context engineering strategies, and optimization methods to build scalable and production-ready LLM systems. The course emphasizes both theor…