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 theoretical foundations and practical workflows using modern tooling such as Hugging Face, Trainer APIs, and model monitoring platforms. By the end of this course, you will be able to: - Explain the principles of transfer learning, model adaptation, and parameter-efficient fine-tuning for large language models - Fine-tune pretrained models using techniques such as LoRA and adapters for domain-specific and task-based applications - Design effective context engineering strategies, including context optimization, compression, and scalable context patterns - Evaluate fine-tuned models using task-appropriate metrics and perform error analysis - Optimize, deploy, monitor, and maintain fine-tuned models for efficient and cost-effective production use This course is ideal for machine learning engineers, AI practitioners, NLP developers, and data scientists who want to move beyond prompt-only interactions and gain practical expertise in adapting and deploying LLMs in real-world systems. A working knowledge of Python, machine learning fundamentals, and basic NLP concepts is recommended to get the most out of this course. Join us to master the end-to-end lifecycle of fine-tuning, optimizing, and operationalizing large language models—from pretrained foundations to scalable, production-ready AI solutions.