AI Document Intelligence: RAG, Agents & ML Data

AI Document Intelligence: RAG, Agents & ML Data

Build a complete AI-powered Document Intelligence platform from scratch and learn how to transform unstructured PDFs into intelligent applications, structured datasets, AI agents, and ML-ready data.

Most AI courses stop at embeddings and question-answering. This course goes much further.

You will build an end-to-end healthcare claims intelligence platform that starts with raw PDF documents and evolves into a production-style system featuring RAG, AI Agents, FastAPI services, React applications, structured datasets, analytics-ready outputs, and machine learning pipelines.

Throughout the course, you will work on a realistic project and implement every major component yourself instead of relying on black-box frameworks.

What You Will Build

• PDF ingestion and document processing pipeline

• Automated text extraction from real-world documents

• Data cleaning and preprocessing workflows

• Intelligent document chunking strategies

• Embedding generation and vector storage using ChromaDB

• Retrieval-Augmented Generation (RAG) applications

• AI Agents capable of selecting and executing tools

• Structured claim datasets generated from unstructured documents

• ML-ready datasets for analytics and machine learning

• FastAPI backend services

• Modern React frontend application

• End-to-end AI Document Intelligence platform

What You Will Learn

• Document Intelligence architecture and design patterns

• RAG implementation from scratch

• Vector databases and semantic search

• ChromaDB integration

• Prompt engineering for retrieval systems

• Agentic AI workflows and tool usage

• Dynamic query planning and execution

• Structured data extraction from PDFs

• Data quality validation and reporting

• FastAPI API development

• React application development

• Building production-style AI applications

• Preparing data for Machine Learning and MLOps workflows

Why This Course Is Different

Most courses teach RAG as an isolated concept.

This course demonstrates how RAG fits into a complete AI ecosystem where documents are processed, validated, transformed into structured data, queried through AI agents, exposed through APIs, visualized in modern web applications, and ultimately prepared for machine learning use cases.

You will understand not only how individual components work, but also how they fit together to create enterprise-grade AI solutions.

Course Statistics

• 11.5+ Hours of Content

• 91+ Lectures

• End-to-End Project-Based Learning

• Real-World Healthcare Claims Use Case

• FastAPI + React Integration

• RAG + Agents + ML Data Pipeline

• Source Code Included

Who This Course Is For

• AI Engineers

• Machine Learning Engineers

• Data Scientists

• Python Developers

• Full Stack Developers

• Solution Architects

• GenAI Practitioners

• Students looking to build real-world AI applications

Prerequisites

• Basic Python knowledge

• Basic understanding of APIs

• Curiosity to learn AI, RAG, Agents, and Document Intelligence

By the end of this course, you will have built a complete AI Document Intelligence platform capable of transforming raw PDFs into searchable knowledge, intelligent agent workflows, structured datasets, analytics-ready outputs, and ML-ready data pipelines.