What you'll learn:
- Access and fine-tune over 100 models on Hugging Face, leveraging 100,000+ datasets
- Save 10+ hours per week by automating your prompts
- Work on a real-world project that covers everything from dataset creation to deployment
- Deploy models easily with Hugging Face using pre-built Gradio templates
- Configure RAG on Google Gemini Pro
- Master RAG concepts with LangChain to build retrieval-augmented systems
- Build every kind of LLM-based application
- Gain hands-on experience in cost-effective model fine-tuning with open-source tools
- Quickly create new datasets in a few simple steps
- Learn fine-tuning techniques used by industry experts
- Explore advanced techniques like Fusion Retrieval and GraphRAG
- Use free GPU resources and hosting
- Learn how to evaluate your model's performance
- Understand how to handle edge cases, biases, and ethical considerations when working with AI models
This course covers everything from Large Language Models (LLMs) and prompt engineering to fine-tuning , as well as advanced concepts like Direct Preference Optimization (DPO). You'll also dive deep into Retrieval-Augmented Generation (RAG), which enhances your LLMs' capabilities by integrating retrieval systems for more accurate and 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 Will Get
In addition to the core topics, our course features in-depth, real-world case studies on fine-tuning, prompt engineering, and Retrieval-Augmented Generation (RAG). These case studies not only highlight cutting-edge techniques but also offer practical, hands-on insights into their application in real-world AI projects. By exploring actual scenarios and projects, learners will gain a deep understanding of how to effectively utilize these methods to solve complex challenges. The case studies are designed to bridge the gap between theory and practice, enabling participants to see how these advanced techniques are deployed in industry settings.
Moreover, these examples provide a step-by-step framework for applying theoretical concepts to real-world applications. Whether it's fine-tuning models for enhanced performance, engineering prompts for improved outputs, or leveraging retrieval systems to augment generation, learners will be able to confidently implement these strategies in their own projects. This ensures that by the end of the course, participants will not only have a solid foundation in generative AI concepts but also the ability to apply them in practical, impactful ways.
