What you'll learn

LLMs (Large Language Models) have general, limited knowledge and training data, and they may produce irrelevant, unsafe, or unhelpful outputs. In this course, Fine-tuning and Customizing LLMs, you’ll learn to pre-train, fine-tune and reinforce the LLM learning to maximize their capabilities and align with human preferences and expectations. First, you’ll explore the training techniques to pre-train the models with supervised fine-tuning , prompt engineering, and reinforcement learning with human feedback. Next, you’ll explore core training techniques, including supervised fine-tuning, prompt engineering, and reinforcement learning with human feedback (RLHF) to adapt pre-trained models for specific tasks and performance goals. Finally, you’ll discover how to evaluate and optimize model performance by applying methods like parameter-efficient fine-tuning (PEFT), transfer learning, and evaluation metrics to improve model reliability and reduce bias. When you’re finished with this course, you’ll have the skills and knowledge of fine-tuning and customizing LLMs that is needed to deliver accurate, context-aware, and human-aligned results across a wide range of AI-driven applications.