Advanced Generative AI

Module - II: AI for Non-Technical Audiences (4 hours)

•   Simplified Explanation of AI Concepts
•   Ethics and Governance in AI
•   Basics of AI Algorithms and Data-Driven Decision-Making
•   AI Applications in Everyday Life

Module - III: Applications of AI Across Industries (6 hours)

•   AI in Healthcare, Finance, Retail, Manufacturing, and Other Sectors
• Case Studies and Real-World Examples
• Identifying AI Opportunities (Interactive Session)

Module IV: Generative AI and Large Language Models (LLMs) (6 hours)

• Introduction to Generative AI
• Survey of LLMs (GPT, BERT, T5, LLama, Gemini, Mistral, Perplexity)
• Hands-on Demonstration (Text, Image, and Media Generation)

Module V: : Python Programming

• Basics of Python Programming
• Control Flow and Decision Making
• Loops in Python
• Functions in Python
• Python Data Structure
• Working with Strings
• File Handling in Python
• Introduction to Modules and Libraries

Module VI: : Project-Based Learning (8 hours)

• Project 1: Creative AI (Storyboarding & Video Generation)
• Project 2: AI in Data Analysis
• Project 3: AI-Driven ERP Solution for Retail

Module VII: Approaches to AI Solutioning (4 hours)

• Frameworks for AI Solutioning
• AI in Product Management
• Agile Methodologies and Design Thinking for AI

Module VIII: Introduction to Robotic Process Automation (RPA) (4 hours)

• Overview of RPA and its Business Applications
• AI-Powered RPA and Intelligent Automation
• Hands-on: Building Simple RPA Bots

Module IX: Emerging Trends and Future of AI (4 hours)

• Explainable AI and Ethical AI
• AI in IoT and Edge Computing
• Capstone Discussion and Q&A

Module X: AI Ethics and Responsible AI (6 hours)

• Introduction to AI Ethics: Principles and Guidelines
• Bias in AI: Detection, Mitigation, and Case Studies
• Explainability and Interpretability in AI Models
• Privacy and Data Security in AI Systems
• Ethical AI Governance Frameworks (EU AI Act, IEEE Standards, etc.)
• Hands-on Session: Evaluating AI Systems for Bias and Fairness

Module XI: Prompt Engineering (4 hours)

• Understanding Prompts and Their Role in A
• Types of Prompting: Zero-Shot, One-Shot, Few-Shot Learning
• Advanced Techniques: Chain-of-Thought (CoT), SelfConsistency, Tree of Thoughts
• Hands-on Exercises: Enhancing Model Outputs with Effective Prompts

Module XII: Retrieval-Augmented Generation (RAG) (6 hours)

• Introduction to RAG and How It Works
• Integrating Vector Databases for Efficient Search (e.g., FAISS, Pinecone)
• Implementing RAG in LLM Applications
• Hands-on Project: Building a Q&A System with RAG

Module XIII: Fine-Tuning LLMs (6 hours)

• Understanding Model Fine-Tuning vs. Pretraining
• Data Preparation and Dataset Selection for Fine-Tuning
• Tools for Fine-Tuning: Hugging Face Transformers, LoRA, QLoRA
• Hands-on: Fine-Tuning a Pretrained LLM on Custom Data

Module XIV: AI Agents and Autonomous Systems (6 hours)

• Introduction to AI Agents and Autonomous Systems
• Multi-Agent Systems and Reinforcement Learning for AI Agents
• Tools for AI Agents: LangChain, Auto-GPT, BabyAGI
• Hands-on: Building an AI Agent for Automating Tasks

Final Capstone Project (8 hours)

  Students apply their learning to create a real-world AI solution

  Options:

• AI-powered Chatbot with RAG
• AI Agent for Workflow Automation
• Fine-Tuned LLM for Industry-Specific Applications

  Presentation and Evaluation