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Getting Started with Document Intelligence
124 students
Getting Started with Document Intelligence
Learn PDF Processing, Text Extraction, Document Cleaning & AI-Ready Data Preparation
Created byRahul Sahay
Last updated 6/2026
English
What you'll learn
- Understand the fundamentals of Document Intelligence and how modern AI systems process unstructured documents
- Extract text from PDF documents and organize files into structured claim-wise document collections
- Combine multiple related documents into consolidated claim files while preserving document context
- Clean and normalize extracted text by removing encoding artifacts, formatting issues, and unwanted whitespace
- Build an end-to-end document processing pipeline using Python for real-world document workflows
- Prepare AI-ready document data that can be used for RAG, AI Agents, Vector Databases, and Machine Learning applications
- Understand the data preparation stage required before implementing embeddings, semantic search, and LLM-powered systems
- Create a foundation for advanced Document Intelligence projects and enterprise-scale AI document processing solutions
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Course content
3 sections • 16 lectures • 1h 53m total length
Requirements
- Basic Python programming knowledge is recommended, but every concept is explained step-by-step.
- No prior experience with RAG, AI Agents, ChromaDB, FastAPI, React, or Document Intelligence is required.
- A computer capable of running Python applications and installing open-source packages.
- Basic understanding of APIs, JSON, and software development concepts will be helpful.
- A willingness to build real-world AI applications through hands-on project-based learning.
- No prior Machine Learning or Data Science experience is required.
- No prior experience with Vector Databases or LLM frameworks is needed.
- Students should be comfortable using VS Code or any Python development environment.
Description
Have you ever wondered how organizations transform thousands of PDFs, invoices, medical records, claims documents, reports, and other unstructured files into data that can be used by AI systems?
Document Intelligence is the answer.
In this course, you will learn the fundamental building blocks of a modern Document Intelligence pipeline by developing a complete end-to-end workflow that transforms raw PDF documents into clean, structured, and AI-ready text data.
Using a real-world claims processing use case, we will build a document processing pipeline from scratch and understand how documents move through various stages before they become ready for downstream AI applications.
Throughout the course, you will learn how to:
Ingest and process PDF documents using Python
Extract text from individual PDF files
Organize documents by claim or business entity
Combine multiple related documents into a single consolidated claim file
Add document boundaries and maintain document context
Clean extracted text and remove encoding artifacts
Normalize whitespace, formatting, and document structure
Prepare high-quality, standardized text data
Build a reusable and scalable document processing workflow
Understand the foundations of enterprise Document Intelligence systems
By the end of this course, you will have built a complete pipeline that takes raw PDFs as input and produces structured, cleaned, and consolidated claim data as output.
More importantly, you will understand the critical preprocessing stage that powers modern AI solutions.
The output generated in this course serves as the foundation for:
Retrieval-Augmented Generation (RAG)
AI Agents
Vector Databases
Semantic Search
Machine Learning Pipelines
Enterprise Document Intelligence Platforms
This course is intentionally focused on the foundational stages of Document Intelligence. Rather than jumping directly into AI models, embeddings, and LLMs, we first build the data pipeline that makes those systems possible.
Who should take this course?
Software Developers
Python Developers
Data Engineers
AI/ML Engineers
Generative AI Enthusiasts
Solution Architects
Anyone interested in Document Intelligence and AI systems
What next after this course?
Once you complete this course, you can continue your learning journey with my comprehensive course:
"AI Document Intelligence: RAG, Agents & ML Data"
In that 12+ hour hands-on course, we take the output generated in this foundation course and extend it into a complete production-style AI Document Intelligence platform. You will learn document chunking, embeddings, vector databases, semantic retrieval, RAG pipelines, AI agents, question-answering systems, structured data generation, and preparation of ML-ready datasets from unstructured documents.
Together, these two courses provide a complete learning path from raw PDFs to AI-powered Document Intelligence applications.
Start your journey today and learn how modern AI systems transform unstructured documents into valuable, actionable intelligence.
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.