Swapan Chakrabarty
The End-to-End Natural Language Processing digital badge is designed to provide the basics of text data and how to process textual data using state-of-the-art AI tools. Participants will leverage large language models for NLP and perform Prompt Engineering and Retrieval Augmented Generation. The End-to-End Natural Language Processing course is offered by Iowa State University's Translational AI Center (TrAC) and is part of a larger Foundations of AI pathway program.
Skills / Knowledge
- Natural Language Processing
- Prompt Engineering
- Large Language Models
- Artificial Intelligence
Issued on
Expires on
Earning Criteria
Required
The End-to-End Natural Language Processing Badge is earned after successful completion of a 4-week, asynchronous, self-paced online course consisting of 4 modules. The 4 modules cover essential topics of natural language processing and how to use these models for a broad range of audiences.
This course offers a blend of hands-on activities, assignments, video lectures and tutorials.
Learning Outcomes:
Outline fundamental NLP techniques such as data preprocessing, tokenization, and Prompting
Implement NLP models using Prompt Engineering, Retrieval-Augmented Generation (RAG), and fine-tuning to perform simple NLP tasks
Evaluate NLP models based on key performance metrics such as accuracy, precision, recall, and F1-score
Design an end-to-end NLP pipeline that includes preprocessing, modeling, evaluation, and deployment for a real-world task
Assessment:
Participants will be assessed on:
Engagement with each module
One coding assignment to build an NLP model
One exercise with prompt engineering and RAG that includes implementing python codes
2 Quizzes assessing basic natural language processing knowledge
About TrAC
The Translational AI Center will break down disciplinary silos to bring together core Iowa State artificial intelligence researchers and subject matter experts interested in applying new technologies to their work. The center will initially focus on conducting core artificial intelligence research, as well as pursuing five application areas of artificial intelligence:
Materials design and manufacturing
Biology, healthcare, and quality of life
Autonomy, intelligent transportation, and smart infrastructure
Food, energy, and water
Ethics, fairness, and adoption.
In addition to serving as a scientific hub for translational artificial intelligence, the center will organize research seminars, host workshops, training, and onboarding programs, offer seed funding for research projects, and serve as an intermediary between private industry partners seeking research services and appropriate university faculty.