Fundamentals: Automate Data Extraction with LLMs

Course Outline

This course teaches the fundamentals of building scalable entity extraction pipelines with LLMs, progressing from basic setup and extraction techniques to advanced topics like batch processing, cloud deployment, real-time data processing, and model performance evaluation

Learning Outcomes

• Establish baseline understanding of entity extraction
• Learn how to extract knowledge and insights from unstructured data with AI
• Create a reusable and scalable data processing pipeline for a variety of use cases

Who Is this course for?

• Developers and AI Enthusiasts interested in building practical AI tools for text data processing.
• Business Professionals looking to leverage AI for tasks such as information retrieval, data structuring, and trend analysis.
• Researchers and Students in AI, NLP, or related fields aiming to build hands-on experience with large language models and data extraction techniques.
• Data Scientists and Machine Learning Engineers seeking to integrate entity extraction pipelines into large-scale projects.

Pre-requisites

• Basic Programming Knowledge: Familiarity with Python programming is beneficial.
• Understanding of AI and NLP Concepts: While not mandatory, a foundational understanding of AI, particularly natural language processing, will enhance your learning experience.
• Interest in Data Processing: Enthusiasm for working with large datasets and extracting structured information from text will be helpful.
• OpenAI Account: An activated account, API configured and account loaded with minimal credits