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

Paulo Dichone

AU$123.99

Video Sep 2024 3hrs 1min 1st Edition

Renews at AU$24.99p/m

Paulo Dichone

AU$123.99

Video Sep 2024 3hrs 1min 1st Edition

Renews at AU$24.99p/m

Renews at AU$24.99p/m

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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.