Project Requirement: Development of a 20-Day Training Program on Generative & Agentic AI Using Python We are seeking an experienced AI/Python developer or trainer to create a comprehensive 20-day training curriculum, with one 30-minute module per day, covering Generative AI and Agentic AI using Python. Deliverables: Complete Training Presentation (PPT): Professionally designed, beginner-to-advanced content that is easy to follow and aligned with current industry standards and trends. Executable Python Code Files: Clean, well-documented raw Python scripts compatible with Visual Studio Code, covering all modules, examples, exercises, and the final project. End-to-End Capstone Project: A modern, trending, real-world project demonstrating practical implementation of Generative and Agentic AI concepts taught throughout the course. Course Scope: Starts from foundational concepts and progresses to advanced applications. Python Coding from begineer till advanced level with main focus areas on Gen AI and Agentic AI development What is AI ? ML? DL ? Neural Network ? Cover a journey for Supervised, Unsupervised, Reinforcement Learning ( Here just touchbase with good examples and Visual PPT) Then Cover how CNN, RNN, LSTM , GAN creating foundation of Generative AI. Cover Transformer Architecture with each component explained . Start of learning LLM Prompt Engineering( Different Prompting techniques , when to use what ? ) RAG architecture Langchain and its modules Chunking strategies vector dB Coding for a real project of using RAG technique(May be a Chatbot) Move towards Deploying LLM application in GCP cloud. AGentic AI Introduction AI Agent main component and best practices to code MCP A2A Protocol Langraph, Google ADK One Agent creation using 1st Langraph and then using Google ADK Develop a Multi-Agentic AI Solution with a project and Deploy in GCP All steps should be covered in README.txt file. Add a video demo to show the same project code are executed. Code should be clean, executable and proper guidelines are given, PPT should be visually appealing. Includes hands-on coding, practical examples, and guided exercises. All Python code should be executable. Designed for learners with no prior background as well as intermediate users looking to advance their skills.