Overview

Master the fundamentals and advanced concepts of generative AI development through a comprehensive 21-hour course covering large language models, data preprocessing, and cutting-edge techniques. Progress from foundational concepts like transformer architectures and ChatGPT training to hands-on projects using industry-standard tools including Hugging Face, OpenAI, and LangChain. Build practical applications ranging from text summarization and custom chatbots to advanced implementations using vector databases like ChromaDB, Pinecone, and Weaviate. Explore open-source LLMs including Llama, Falcon, and Mistral, while learning essential techniques such as RAG (Retrieval Augmented Generation), fine-tuning, and prompt engineering. Gain expertise in deploying AI applications through CI/CD on AWS, working with Google Cloud Vertex AI, and implementing end-to-end projects including medical chatbots, financial analysis tools, and source code analyzers. Complete multiple hands-on projects utilizing various AI platforms and tools, culminating in practical experience with AWS Bedrock and enterprise-level LLMOps implementations.

Syllabus

Course Introduction
Introduction of the Instructor
Introduction to Generative AI
End to end Generative AI Pipeline
Data Preprocessing & cleaning
Data representation & vectorization for the model training
Text Classification Practical
Introduction to Large Language Models & its architecture
In depth intuition of Transformer-Attention all your need Paper
How ChatGPT is trained
Introduction of Hugging Face
Hands-On Hugging Face - Transformers, HF Pipeline, Datasets, LLMs
Data processing,Tokenizing and Feature Extraction with hugging face
Fine-tuning using a pretrain models
Hugging face API key generation
Project: Text summarization with hugging face
Project: Text to Image generation with LLM with hugging face
Project: Text to speech generation with LLM with hugging face
Introduction to OpenAI
How to generate OpenAI API key?
Local Environment Setup
Hands on OpenAI - ChatCompletion API and Completion API
Function Calling in OpenAI
Project: Telegram bot using OpenAI
Project: Finetuning of GPT-3 model for text classification
Project: Audio Transcript Translation with Whishper
Project: Image genration with DALL-E
Mastering Prompt Engineering
The Complete Introduction to Vector Databases
Mastering Vector Databases with ChromaDB
Mastering Vector Databases with Pinecone
Mastering Vector Databases with Weaviate
Introduction & Installation and setup of langchain
Prompt Templates in Langchain
Chains in Langchain
Langchain Agents and Tools
Memory in Langchain
Documents Loader in Langchain
Multi-Dataframe Agents in Langchain
How to use Hugging face Open Source LLM with Langchain
Project: Interview Questions Creator Application
Project: Custom Website Chatbot
Introduction to Open Source LLMs - Llama
How to use open source llms with Langchain
Custom Website Chatbot using Open source LLMs
Open Source LLMs - Falcon
Introduction & Importance of RAG
RAG Practical demo
RAG Vs Fine-tuning
Build a Q&A App with RAG using Gemini Pro and Langchain
What is Fine Tuning? Parameter Efficient Fine-Tuning - LoRA & QLoRA
Fine-Tuning Meta Llama 2 on Custom Data
Introduction to LlamaIndex & end to end Demo
Open Source Mistral LLM with LlamaIndex
Project: Financial Stock Analysis using LlamaIndex
Project: End to End Medical Chatbot with LLM, Pinecone, LangChain
Project: End to End Source Code Analysis with LangChain, LLM and ChromaDB
Project: Implementing Zomato chatbot with Chainlit
How to Deploy Generative AI Application as CICD on AWS
Introduction to LLMOps & Why we need it?
Generative AI with Google Cloud Vertex AI a LLMOps Platform
Vertex AI Hands-On on Google Cloud
Vertex AI Local Setup - Run Gemini Pro on Local Machine
RAG on Vertex AI with Vector Search and Gemini Pro
LLM powered application on Vertex AI
Fine-tuning Foundation Model on VertexAI
Introduction to AWS Bedrock
End to End RAG Project using AWS Bedrock

Taught by

freeCodeCamp.org

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