In this comprehensive Generative AI course from @dswithbappy, you'll dive deep into the world of generative AI, exploring key concepts such as large language models, data preprocessing, and advanced techniques like fine-tuning and RAG. Through hands-on projects with tools like Hugging Face, OpenAI, and LangChain, you’ll build real-world applications from text summarization to custom chatbots. By the end, you'll have mastered AI pipelines, vector databases, and deployment techniques using platforms like Google Cloud Vertex AI and AWS Bedrock. 💻 Code and resources: https://github.com/entbappy/Generative-AI-Mastery-Resources Boktiar's other channel and LinkedIn: https://www.youtube.com/@CodeCommander-d6t https://www.linkedin.com/in/boktiarahmed73/ ⭐️ Contents ⭐️ 0:00:00 Course Introduction 0:04:36 Introduction of the Instructor 0:05:52 Introduction to Generative AI 0:23:51 End to end Generative AI Pipeline 0:59:53 Data Preprocessing & cleaning 1:25:12 Data representation & vectorization for the model training 2:28:57 Text Classification Practical 2:42:05 Introduction to Large Language Models & its architecture 3:03:28 In depth intuition of Transformer-Attention all your need Paper 3:33:19 How ChatGPT is trained 3:43:44 Introduction of Hugging Face 3:56:02 Hands-On Hugging Face - Transformers, HF Pipeline, Datasets, LLMs 4:10:20 Data processing,Tokenizing and Feature Extraction with hugging face 4:17:34 Fine-tuning using a pretrain models 4:30:19 Hugging face API key generation 4:31:40 Project: Text summarization with hugging face 4:54:56 Project: Text to Image generation with LLM with hugging face 5:09:45 Project: Text to speech generation with LLM with hugging face 5:12:14 Introduction to OpenAI 5:21:12 How to generate OpenAI API key? 5:24:46 Local Environment Setup 5:27:13 Hands on OpenAI - ChatCompletion API and Completion API 5:54:44 Function Calling in OpenAI 6:09:11 Project: Telegram bot using OpenAI 6:44:18 Project: Finetuning of GPT-3 model for text classification 6:54:51 Project: Audio Transcript Translation with Whishper 7:12:54 Project: Image genration with DALL-E 7:18:22 Mastering Prompt Engineering 7:42:49 The Complete Introduction to Vector Databases 8:11:05 Mastering Vector Databases with ChromaDB 8:54:45 Mastering Vector Databases with Pinecone 9:19:39 Mastering Vector Databases with Weaviate 9:35:25 Introduction & Installation and setup of langchain 9:51:32 Prompt Templates in Langchain 9:55:33 Chains in Langchain 10:05:01 Langchain Agents and Tools 10:10:29 Memory in Langchain 10:17:41 Documents Loader in Langchain 10:21:25 Multi-Dataframe Agents in Langchain 10:25:50 How to use Hugging face Open Source LLM with Langchain 10:32:00 Project: Interview Questions Creator Application 11:29:26 Project: Custom Website Chatbot 11:46:21 Introduction to Open Source LLMs - Llama 12:20:39 How to use open source llms with Langchain 12:41:59 Custom Website Chatbot using Open source LLMs 13:12:41 Open Source LLMs - Falcon 13:29:11 Introduction & Importance of RAG 13:34:43 RAG Practical demo 13:45:10 RAG Vs Fine-tuning 13:48:31 Build a Q&A App with RAG using Gemini Pro and Langchain 13:57:35 What is Fine Tuning? Parameter Efficient Fine-Tuning - LoRA & QLoRA 14:10:07 Fine-Tuning Meta Llama 2 on Custom Data 14:28:55 Introduction to LlamaIndex & end to end Demo 14:57:41 Open Source Mistral LLM with LlamaIndex 15:09:57 Project: Financial Stock Analysis using LlamaIndex 15:23:51 Project: End to End Medical Chatbot with LLM, Pinecone, LangChain 16:34:38 Project: End to End Source Code Analysis with LangChain, LLM and ChromaDB 17:06:31 Project: Implementing Zomato chatbot with Chainlit 17:40:11 How to Deploy Generative AI Application as CICD on AWS 18:10:32 Introduction to LLMOps & Why we need it? 18:35:06 Generative AI with Google Cloud (Vertex AI) a LLMOps Platform 18:49:37 Vertex AI Hands-On on Google Cloud 19:13:36 Vertex AI Local Setup - Run Gemini Pro on Local Machine 19:24:59 RAG on Vertex AI with Vector Search and Gemini Pro 19:41:06 LLM powered application on Vertex AI 19:44:57 Fine-tuning Foundation Model on VertexAI 19:57:51 Introduction to AWS Bedrock 20:14:52 End to End RAG Project using AWS Bedrock