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Preprocessing Unstructured Data for LLM Applications

Matthew Robinson

Enhancing a RAG system’s performance depends on efficiently processing diverse unstructured data sources.

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Enhancing a RAG system’s performance depends on efficiently processing diverse unstructured data sources.

In this course, you’ll learn techniques for representing all sorts of unstructured data, like text, images, and tables, from many different sources and implement them to extend your LLM RAG pipeline to include Excel, Word, PowerPoint, PDF, and EPUB files.

1. How to preprocess data for your LLM application development, focusing on how to work with different document types.

2. How to extract and normalize various documents into a common JSON format and enrich it with metadata to improve search results.

3. Techniques for document image analysis, including layout detection and vision transformers, to extract and understand PDFs, images, and tables.

4. How to build a RAG bot that is able to ingest different documents like PDFs, PowerPoints, and Markdown files.

Apply the skills you’ll learn in this course to real-world scenarios, enhancing your RAG application and expanding its versatility.

Enroll now

What's inside

Syllabus

Preprocessing Unstructured Data for LLM Applications
Enhancing a RAG system’s performance depends on efficiently processing diverse unstructured data sources. In this course, you’ll learn techniques for representing all sorts of unstructured data, like text, images, and tables, from many different sources and implement them to extend your LLM RAG pipeline to include Excel, Word, PowerPoint, PDF, and EPUB files. Join this course and learn: 1. How to preprocess data for your LLM application development, focusing on how to work with different document types. 2. How to extract and normalize various documents into a common JSON format and enrich it with metadata to improve search results. 3. Techniques for document image analysis, including layout detection and vision transformers, to extract and understand PDFs, images, and tables. 4. How to build a RAG bot that is able to ingest different documents like PDFs, PowerPoints, and Markdown files. Apply the skills you’ll learn in this course to real-world scenarios, enhancing your RAG application and expanding its versatility.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Helps learner understand data preprocessing for LLM applications, expanding the range of data sources that can be used and enhancing the performance of the LLM
Develops key document analysis techniques, including layout detection and vision transformers, which are vital for extracting and understanding the content of a wide range of document types
Covers strategies for expanding the LLM RAG pipeline to ingest and process various document formats, including PDFs, PowerPoint presentations, and Markdown files
Provides learners with practical experience in applying data representation techniques and normalization for a common JSON format, enhancing the search results and relevance
Requires learners to have a foundational understanding of LLM application development, which may limit accessibility for beginners

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Preprocessing Unstructured Data for LLM Applications with these activities:
Review foundational knowledge on NLP and ML
Start the course with a strong foundation in the underlying NLP and ML concepts to enhance your learning experience.
Browse courses on Natural Language Processing
Show steps
  • Revisit your notes or textbooks on NLP and ML core concepts.
  • Review online articles or tutorials to refresh your understanding.
  • Complete practice problems or exercises to test your knowledge.
Review the basics of Python programming
Ensure a strong foundation in Python programming to enhance your ability to follow along with the course material.
Browse courses on Python
Show steps
  • Go through online tutorials or documentation to refresh your memory on Python syntax.
  • Solve coding challenges or practice problems.
  • Build a small Python project to apply your refreshed skills.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron
Gain a comprehensive understanding of machine learning concepts and techniques.
View Hands-On Machine Learning with Scikit-Learn,... on Amazon
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  • Read the book and take notes.
  • Work through the exercises in the book.
  • Apply the concepts you learn to your own projects.
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Join a study group to discuss the course material and work on projects together
Collaborate with your peers to reinforce your understanding of the course material and gain diverse perspectives.
Show steps
  • Find a study group or create your own.
  • Meet regularly to discuss the course material.
  • Work together on projects and assignments.
Solve coding exercises on text data preprocessing
Strengthen your skills in preprocessing text data, a crucial step for effective RAG system development.
Browse courses on Text Preprocessing
Show steps
  • Find coding exercises or practice problems that focus on text preprocessing.
  • Implement data cleaning techniques such as removing stop words, stemming, and lemmatization.
  • Experiment with different text representation methods like bag-of-words and TF-IDF.
Practice using transformer models to represent text
Build fluency and comfort with the core concepts of representing text using transformer models.
Browse courses on NLP
Show steps
  • Complete the interactive exercises on the Hugging Face website.
  • Experiment with different transformer models using the Transformers library in Python.
Build a simple RAG application with limited document types
Apply your acquired knowledge by creating a basic RAG system, reinforcing your understanding of the core concepts.
Browse courses on Information Retrieval
Show steps
  • Choose a simple document type, such as text files or PDFs.
  • Implement basic text processing and indexing techniques.
  • Build a simple search and retrieval interface.
  • Evaluate the performance of your RAG application.
Learn about document image analysis using OpenCV
Gain practical experience in extracting and understanding information from document images.
Show steps
  • Follow the OpenCV tutorials on document image analysis.
  • Build a simple document image analysis application using OpenCV.
Explore vision transformers for document image analysis
Gain practical experience in using vision transformers for document image analysis.
Show steps
  • Find a tutorial on using vision transformers for document image analysis
  • Follow the tutorial and implement the techniques
  • Test the techniques on a set of sample documents
Develop a presentation on the challenges and solutions for processing unstructured data using RAG systems
Demonstrate your in-depth knowledge of the intricacies and solutions for handling unstructured data with RAG systems.
Show steps
  • Research the challenges and solutions for processing unstructured data using RAG systems.
  • Create an outline for your presentation.
  • Develop the content of your presentation.
  • Practice delivering your presentation.
  • Present your findings to your peers or colleagues.
Explore tutorials on document image analysis techniques
Develop proficiency in analyzing and extracting data from various document formats, expanding your RAG system's capabilities.
Show steps
  • Search for online tutorials or courses on document image analysis.
  • Follow the tutorials to learn about layout detection, OCR, and other techniques.
  • Apply the techniques to sample documents to gain hands-on experience.
Create a blog post on how to build a RAG bot using different document types
Solidify your understanding of RAG bots and document processing by sharing your knowledge with others.
Show steps
  • Write a detailed outline for your blog post.
  • Research and gather information on RAG bots and document processing.
  • Write the first draft of your blog post.
  • Edit and revise your blog post.
  • Publish your blog post on a platform like Medium or your own website.
Build a simple question-answering system using a RAG model
Put your skills to the test and reinforce your understanding of RAG models by building a practical application.
Show steps
  • Define the scope and requirements of your project.
  • Gather and prepare the necessary data.
  • Train a RAG model on the data.
  • Evaluate the performance of your model.
  • Deploy your model and make it available to users.

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Course image
Effort
1 hour
Level
Introductory
Via
Coursera
Institution
DeepLearning.AI
Instructor
Matthew Robinson
Session
August 19, 2024 - September 2, 2024
Language
English
Good to know
Helps learner understand data preprocessing for LLM applications, expanding the range of data sources that can be used and enhancing the performance of the LLM
Develops key document analysis techniques, including layout detection and vision transformers, which are vital for extracting and understanding the content of a wide range of document types
Covers strategies for expanding the LLM RAG pipeline to ingest and process various document formats, including PDFs, PowerPoint presentations, and Markdown files
Provides learners with practical experience in applying data representation techniques and normalization for a common JSON format, enhancing the search results and relevance
Requires learners to have a foundational understanding of LLM application development, which may limit accessibility for beginners
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