GenAI World - LLM, Fine-tuning, RAG & Prompt engineering

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What you'll learn:
Understand the fundamentals of prompting in the context of large language models (LLMs).
Learn the importance of prompt engineering for optimizing model perform
Explore advanced concepts like Direct Preference Optimization (DPO) and Parameter-Efficient Fine-Tuning (PEFT).
Gain insights into Retrieval Augmented Generation (RAG), understanding its components and how it enhances LLM capabilities.
Requirements:
Yes, students should have: A foundational understanding of artificial intelligence and machine learning concepts, especially related to language models. Proficiency in Python programming, as the course includes detailed code examples and exercises. Familiarity with deep learning frameworks. Basic knowledge of natural language processing (NLP) and transformer models. Access to necessary computational resources, such as a GPU-enabled environment.
Description:
This course covers everything from Large Language Models (LLMs), prompt engineering to parameter-efficient fine-tuning (PEFT) and advanced concepts like Direct Preference Optimization (DPO). You'll also dive deep into Retrieval Augmented Generation (RAG) to enhance your LLMs' capabilities by integrating retrieval systems for superior responses.By the end of this course, you'll be equipped to create AI solutions that align perfectly with human intent and outperform standard models. What You'll Learn:Craft powerful and effective prompts for LLMs to optimize outputs.Master Direct Preference Optimization (DPO) and PEFT for domain-specific fine-tuning.Implement Retrieval Augmented Generation (RAG) to elevate model performance.Gain insights into state-of-the-art LLM capabilities, focusing on practical and advanced techniques.Develop customized solutions with hands-on code examples and exercises. What you will Get A foundational understanding of artificial intelligence and machine learning concepts, especially related to language models. Proficiency in Python programming, as the course includes detailed code examples and exercises. Familiarity with deep learning frameworks. Basic knowledge of natural language processing (NLP) and transformer models. Access to necessary computational resources, such as a GPU-enabled environment. In addition to the core topics, our course also features real-world case studies on fine-tuning, prompt engineering, and Retrieval Augmented Generation (RAG). These case studies offer practical, hands-on insights into how these techniques are applied in real AI projects .These case studies provide a practical framework for applying the theoretical concepts covered in the course, helping learners implement these methods in their own projects.
Who this course is for:
This course is ideal for: Machine learning engineers and data scientists looking to enhance their skills in fine-tuning large language models. AI researchers and practitioners interested in advanced techniques like RAG, PEFT, and QLoRA. Developers and programmers aiming to implement AI solutions that require domain-specific model customization. Students and academics studying artificial intelligence, machine learning, or natural language processing. Anyone interested in state-of-the-art AI technologies and how to apply them effectively in real-world scenarios.
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