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/Generativ…

Boktiar’s other channel and LinkedIn:
/ @codecommander-d6t
/ boktiarahmed73

More courses: https://euron.one/

⭐️ 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