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Introduction
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Classroom Contents
Generative AI Full Course 2024 - From Basics to Advanced Applications
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- 1 Introduction
- 2 Introduction to Generative AI
- 3 Advantages of Generative AI
- 4 The Future of Generative AI
- 5 Ethical Considerations in Generative AI
- 6 Introduction and Phases to LLMs
- 7 Introduction to OpenAPI GPT API
- 8 Claude 3.5 Sonnet
- 9 Claude Artifacts
- 10 Demo on Claude Artifacts
- 11 Use cases of Claude
- 12 Demo on Claude Sonnet
- 13 GPT 4o Mini and uses
- 14 Why GPT 4o Mini
- 15 Features of GPT 4o Mini
- 16 Difference between GPT 4o and GPT 4o Mini
- 17 Demo on Playground tab, Dashboard tab, Docs tab and API References tab
- 18 Prompting on Playground and Billing Settings
- 19 Version of Google Gemini
- 20 General Prompt Demo Google AI Studio
- 21 Structured Prompt in Google AI Studio
- 22 Model Tuning in Google AI Studio using System Sample
- 23 Other data import options in Google AI Studio using System Sample
- 24 Abstract of the Email Generator App
- 25 Software Requirements for App
- 26 Implementation of the App
- 27 Executing the App
- 28 Generative AI Popular Tools
- 29 ChatGPT
- 30 Github Copilot
- 31 Claude
- 32 Gemini
- 33 Basics of Prompt Engineering
- 34 Basics of ChatGPT
- 35 Demonstration of Prompt on ChatGPT
- 36 Python App using ChatGPT 4o
- 37 Using ChatGPT 4o for Statistical Analysis
- 38 Demonstration of Prompt using ChatGPT 4-o
- 39 Portfolio website code execution
- 40 Introduction to Github and Github Copilot
- 41 Hands-on session on Github Copilot
- 42 Introduction of Claude
- 43 Prompt Engineering and Install Claude
- 44 Hands-on Claude
- 45 Program for tic-toe game using Claude
- 46 Claude 2 API
- 47 Integration of Python and Gemini 1.5 pro
- 48 Chatbot using Gemini 1.5 pro
- 49 Generative AI Applications
- 50 Flask ChatGPT App
- 51 Flask Text-to-Image App
- 52 Demo - Flask Text-to-Image App
- 53 Introduction to Langchain
- 54 Why LangChain?
- 55 Development Environment Setup of LangChain
- 56 Demo on Library Installation
- 57 LangChain Core Concepts
- 58 LangChain Components
- 59 LangChain Case Study
- 60 Limitations of LLMs
- 61 Introduction to RAG
- 62 RAG Basics Concepts and Terminology
- 63 Key Components of RAG - Retrieval and Generation
- 64 Workflow and Applications of RAG
- 65 Hallucinations in RAG
- 66 Steps to implement RAG with LangChain
- 67 Hands-on RAG in detail
