AI Document Intelligence: RAG, Agents & ML Data

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About this course

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 BuildPDF 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 ChromaDBRetrieval-Augmented Generation (RAG) applicationsAI Agents capable of selecting and executing tools

Structured claim datasets generated from unstructured documentsML-ready datasets for analytics and machine learning

FastAPI backend services

Modern React frontend application

End-to-end AI Document Intelligence platform

What You Will LearnDocument Intelligence architecture and design patternsRAG 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 PDFsData quality

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