Event Details
Event Title Foundations of AI: Basics of Natural Language Processing
Location Zoom
Sponsor RENCI - National Consortium for Data Science (NCDS) + RENCI
Date/Time 05/15/2025 1:00 PM - 5:00 PM
Event Price
For more information, contact the event administrator: Amanda Miller acamanda@email.unc.edu
Event Presenters
Name Title  
Amy Hemmeter Amy Hemmeter is a Sr. Manager of Data Science in Natural Language Processing at Workhuman. She has over 5 years of experience in industry in NLP as a Data Scientist, Machine Learning Engineer, Manager and Lecturer. She has worked on projects ranging from Conversational AI to Information Retrieval to Generative AI. She received her MSA from the Institute for Advanced Analytics at North Carolina State University in 2018, where she has also taught an annual workshop on Natural Language Processing since 2020. As someone who transitioned into data science and NLP from a linguistics background (an MA she also earned at NCSU in 2016), she cares deeply about translating complex topics in data science into information that students from all backgrounds can understand, as well as making that information relevant and practical to students who wish to make a career in industry.
 

In this workshop you’ll learn the basics of Natural Language Processing (NLP): the subfield of AI and data science dedicated to human language. 

You’ll also learn 

-- How to do basic analyses with unstructured text data
-- How to use deep learning to create NLP models from scratch and finally how 
-- How large language models (LLMs) and Generative AI work and connect to other NLP skills.

We’ll touch on some aspects of deep learning that are specific to NLP, some more basic analyses of unstructured data and how to deal with lots of unlabeled text (e.g. clustering) and basically go deeper on things that are really NLP-related as opposed to AI more generally. 

Pre-requisites: Basic Python knowledge required, machine learning knowledge helpful but not required, very basic data science knowledge (i.e. linear regression) is recommended.


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