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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this comprehensive course, you'll learn how to build and fine-tune large language models (LLMs) for real-world applications. Starting with fundamental concepts, you'll progress through hands-on projects that focus on document-based retrieval-augmented generation (RAG) systems, LangChain integration, and fine-tuning techniques. You'll gain the skills to build custom applications like a PDF RAG system, a voice assistant, and a YouTube video summarizer, with a focus on optimizing the retrieval and generation of content. With a blend of theoretical lessons and practical exercises, this course ensures you master both building and fine-tuning LLMs for various AI-driven tasks. You'll also dive deep into advanced fine-tuning methods like LoRA (Low-Rank Adaptation), learning to fine-tune models efficiently with minimal computational resources. Throughout the course, you'll implement real-world projects that integrate sophisticated LLM functionalities into usable applications. By the end of the course, you’ll be capable of deploying and fine-tuning LLMs for personalized tasks, giving you the tools to tackle complex AI challenges in your own projects. This course is designed for intermediate to advanced learners with prior programming experience. It’s perfect for those who want to deepen their understanding of LLMs and apply them to solve industry-specific problems. No prior experience with fine-tuning is required, though knowledge of Python and machine learning basics will be beneficial. By the end of the course, you will be able to build, fine-tune, and deploy LLM applications, including RAG systems, voice assistants, and specialized chatbots, using advanced techniques such as LoRA fine-tuning.