Generative AI: From Fundamentals to Advanced Applications

This comprehensive course is designed to equip learners with a deep understanding of Generative AI, particularly focusing on Large Language Models (LLMs) and their applications. You will delve into the core concepts, practical implementation techniques, and ethical considerations surrounding this transformative technology.

What You Will Learn:

• Foundational Knowledge: Grasp the evolution of AI, understand the core principles of Generative AI, and explore its diverse use cases.
• LLM Architecture and Training: Gain insights into the architecture of LLMs, their training processes, and the factors influencing their performance.
• Prompt Engineering: Master the art of crafting effective prompts to maximize LLM capabilities and overcome limitations.
• Fine-Tuning and Optimization: Learn how to tailor LLMs to specific tasks through fine-tuning and explore techniques like PEFT and RLHF.
• RAG and Real-World Applications: Discover how to integrate LLMs with external knowledge sources using Retrieval Augmented Generation (RAG) and explore practical applications.
• Ethical Considerations: Understand the ethical implications of Generative AI and responsible AI practices.

By the end of this course, you will be equipped to build and deploy robust Generative AI solutions, addressing real-world challenges while adhering to ethical guidelines. Whether you are a data scientist, developer, or business professional, this course will provide you with the necessary skills to thrive in the era of Generative AI.

Course Structure:

The course is structured into 12 sections, covering a wide range of topics from foundational concepts to advanced techniques. Each section includes multiple lectures, providing a comprehensive learning experience.

• Section 1: Introduction to Generative AI
• Section 2: LLM Architecture and Resources
• Section 3: Generative AI LLM Lifecycle
• Section 4: Prompt Engineering Setup
• Section 5: LLM Properties
• Section 6: Prompt Engineering Basic Guidelines
• Section 7: Better Prompting Techniques
• Section 8: Full Fine Tuning
• Section 9: PEFT – LORA
• Section 10: RLHF
• Section 11: RAG
• Section 12: Generative AI for Vision (Preview)