AI Document Intelligence: RAG, Agents & ML Data | Udemy [Update 06/2026]
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Genre: eLearning[/center]

Build a complete AI app that turns PDFs into RAG answers, agentic insights, APIs, UI, and ML-ready data.

What you'll learn:
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[*]Build an end-to-end AI Document Intelligence platform using PDFs, RAG, AI Agents, FastAPI, React, and ML-ready datasets.
[*]Implement Retrieval-Augmented Generation (RAG) using ChromaDB, embeddings, semantic search, and grounded AI responses.
[*]Design and develop AI Agents that select tools, execute queries, and generate intelligent responses from documents and data.
[*]Transform unstructured PDF documents into structured, analytics-ready, and machine-learning-ready datasets.
[*]Build production-style FastAPI backend services and expose AI, RAG, and Agent capabilities through REST APIs.
[*]Develop a modern React application to interact with RAG pipelines, AI Agents, and document intelligence workflows.
[*]Implement document ingestion, text extraction, cleaning, chunking, embeddings, and vector database pipelines from scratch.
[*]Apply real-world AI architecture patterns used in enterprise document intelligence, healthcare, and knowledge systems.
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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
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[*]PDF ingestion and document processing pipeline
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[*]Automated text extraction from real-world documents
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[*]Data cleaning and preprocessing workflows
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[*]Intelligent document chunking strategies
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[*]Embedding generation and vector storage using ChromaDB
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[*]Retrieval-Augmented Generation (RAG) applications
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[*]AI Agents capable of selecting and executing tools
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[*]Structured claim datasets generated from unstructured documents
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[*]ML-ready datasets for analytics and machine learning
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[*]FastAPI backend services
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[*]Modern React frontend application
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[*]End-to-end AI Document Intelligence platform
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What You Will Learn
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[*]Document Intelligence architecture and design patterns
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[*]RAG implementation from scratch
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[*]Vector databases and semantic search
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[*]ChromaDB integration
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[*]Prompt engineering for retrieval systems
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[*]Agentic AI workflows and tool usage
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[*]Dynamic query planning and execution
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[*]Structured data extraction from PDFs
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[*]Data quality validation and reporting
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[*]FastAPI API development
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[*]React application development
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[*]Building production-style AI applications
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[*]Preparing data for Machine Learning and MLOps workflows
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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
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[*]11.5+ Hours of Content
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[*]91+ Lectures
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[*]End-to-End Project-Based Learning
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[*]Real-World Healthcare Claims Use Case
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[*]FastAPI + React Integration
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[*]RAG + Agents + ML Data Pipeline
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[*]Source Code Included
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Who This Course Is For
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[*]AI Engineers
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[*]Machine Learning Engineers
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[*]Data Scientists
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[*]Python Developers
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[*]Full Stack Developers
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[*]Solution Architects
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[*]GenAI Practitioners
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[*]Students looking to build real-world AI applications
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Prerequisites
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[*]Basic Python knowledge
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[*]Basic understanding of APIs
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[*]Curiosity to learn AI, RAG, Agents, and Document Intelligence
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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.

Who this course is for:
Python developers who want to build real-world RAG and Agentic AI applications.
AI Engineers and GenAI practitioners looking to move beyond basic chatbot implementations.
Machine Learning Engineers who want to create ML-ready datasets from unstructured documents.
Data Scientists interested in Document Intelligence, knowledge extraction, and AI automation.
Full Stack Developers who want to integrate FastAPI, React, RAG, and AI Agents into modern applications.
Software Architects and Technical Leads exploring enterprise AI solution design patterns.
Backend Developers interested in vector databases, semantic search, and AI-powered APIs.
Anyone looking to build end-to-end AI Document Intelligence platforms using RAG, Agents, FastAPI, React, and structured data pipelines.

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