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Preprocessing Unstructured Data for LLMs and RAG Systems
Preprocessing Unstructured Data for LLMs and RAG Systems

Preprocessing Unstructured Data for LLMs and RAG Systems: Unlock the Power of Unstructured Data for LLMs and Retrieval-Augmented Generation Systems.

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Profile Icon Paulo Dichone
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NZ$131.99
Video Sep 2024 3hrs 1min 1st Edition
Video
NZ$131.99
Subscription
Free Trial
Arrow left icon
Profile Icon Paulo Dichone
Arrow right icon
NZ$131.99
Video Sep 2024 3hrs 1min 1st Edition
Video
NZ$131.99
Subscription
Free Trial
Video
NZ$131.99
Subscription
Free Trial

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Key benefits

  • Development setup and API configuration for efficient data preprocessing workflows.
  • Advanced data preprocessing techniques for PDFs, HTML, and PPTX using the Unstructured Framework.
  • Build a RAG system for intelligent interaction with complex documents, images, and tables.

Description

This course offers an in-depth exploration of preprocessing unstructured data for large language models and retrieval-augmented generation systems. You’ll start by setting up your development environment and configuring essential APIs, ensuring a solid technical foundation. Next, you’ll dive into data preprocessing techniques, tackling challenges like content extraction, cleaning, and data normalization, making your data ready for advanced AI models. As you progress, the course provides hands-on experience with various document types such as PDFs, HTML, and PPTX files. You’ll learn to transform these unstructured formats into structured data that AI systems can easily process. Advanced modules cover chunking, metadata extraction, and handling complex documents using cutting-edge techniques like visual transformers and document layout detectors. The final section guides you in building a complete RAG system using the skills acquired throughout the course. You’ll preprocess diverse documents, implement semantic similarity searches, and save elements to a vector database. By the end, you’ll be equipped to create intelligent data pipelines and interact with your documents using AI, significantly enhancing your data-driven projects.

Who is this book for?

This course is ideal for data scientists, machine learning engineers, and AI developers who want to enhance their skills in data preprocessing for LLMs and RAG systems. Prerequisites include basic knowledge of Python programming, familiarity with APIs, and a general understanding of machine learning concepts.

What you will learn

  • Configure a complete data preprocessing environment.
  • Extract and clean data from various document types.
  • Normalize and chunk data for efficient processing.
  • Perform metadata extraction and semantic analysis.
  • Develop a full Retrieval-Augmented Generation system.
  • Interact with processed documents using advanced AI tools.

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 30, 2024
Length: 3hrs 1min
Edition : 1st
Language : English
ISBN-13 : 9781836642930
Category :

What do you get with a video?

Product feature icon Download this video in MP4 format
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want

Product Details

Publication date : Sep 30, 2024
Length: 3hrs 1min
Edition : 1st
Language : English
ISBN-13 : 9781836642930
Category :

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Table of Contents

8 Chapters
Introduction Chevron down icon Chevron up icon
Development Environment Setup Chevron down icon Chevron up icon
Data Preprocessing for LLMs - Deep Dive Chevron down icon Chevron up icon
Hands-on: The Unstructured Framework - Preprocessing HTML, PDFs & PPTX Documents Chevron down icon Chevron up icon
Chunking and Metadata Extraction Chevron down icon Chevron up icon
Preprocessing Complex Documents - PDFs and Images Chevron down icon Chevron up icon
Build a RAG System Using Learned Techniques - Full Use Case Chevron down icon Chevron up icon
Wrap up Chevron down icon Chevron up icon
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