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End-to-End Natural Language Processing

September 29 @ 8:00 am – November 30 @ 5:00 pm

Course Description

Elevate your machine learning skills with our comprehensive course, “End-to-End Natural Language Processing”. This course covers everything you need to know about text data processing using state-of-the-art AI tools and various NLP tasks. Learn how to leverage large language models, master prompt engineering and retrieval augmented generation (RAG), and create your own specialized AI models for specific tasks.

In this course, you will engage in hands-on activities, homework, and instructor consulting to make learning natural language processing enjoyable and rewarding. You will tackle real-world problems in NLP while receiving expert guidance from our instructors. By the end of this course, you’ll have the skills and confidence to tackle any task with natural language processing. Join us and become a leader in the rapidly evolving field of NLP and AI!

Course at a Glance

Course Hours: 16 hours

Instructional Period: September 29 – October 26, 2025

Time to Complete Badge: 60 days

Last Dy to Earn Badge: November 30, 2025

Expertise Level: Beginner/Intermediate

This course is part of the Foundational AI track in the TrAC Micro-Credential pathway at Iowa State University.

Foundational AI courses

Mastering PyTorch

End-to-End Computer Vision

End-to-End Natural Language Processing

Generative Models

MLOps

Interpretability in AI

Learn more about TrAC Microcredential Courses!

Prerequisites & intended Audience

Prerequisites:
Basic Python & PyTorch programming
Basic understanding of deep learning


Intended Audience:
The course is intended for a broad audience within the spectrum of the software and technology industry, including software engineers, data scientists, data engineers, data analysts, research scientists, and software developers. The course is designed to provide a comprehensive understanding of natural language processing and how to use these models for a broad range of audiences and applications.

Learning Outcomes

By the end of the course, you should be able to:

Design an end-to-end NLP pipeline that includes preprocessing, modeling, evaluation, and deployment for real-world tasks such as automated FAQ answering, document summarization, or chatbot development

Outline fundamental NLP techniques such as data preprocessing, tokenization, and prompting for zero-shot learning tasks

Implement NLP models using prompt engineering, Retrieval-Augmented Generation (RAG), and fine-tuning to perform simple NLP tasks including automated workflows

Evaluate NLP models based on key performance metrics such as accuracy, precision, recall, and F1-score, comparing traditional and transformer-based approaches

Assessments
  • 2 Quizzes covering NLP fundamentals, text preprocessing techniques, tokenization, and prompting
  • 1 Coding Exercise with prompt engineering and RAG implementation, including development of an agentic AI pipeline to automate NLP workflows
  • 1 Final Project to design and deploy a complete end-to-end NLP pipeline for a real-world application (e.g., automated FAQ answering, legal document summarization, or chatbot)

Course Outline
  • Module 1: NLP Fundamentals and Data Processing
  • Introduction to NLP applications and text data types
  • Text preprocessing techniques (cleaning, tokenization, normalization)
  • Zero-shot learning and prompting techniques with Large Language Models
  • Module 2: Common NLP Tasks and Model Implementation
  • Prompt engineering for LLM-based solutions
  • Retrieval-Augmented Generation (RAG) pipeline construction
  • Fine-tuning transformer models (e.g., BERT) for specific tasks
  • Developing agentic AI workflows for automated NLP tasks
  • Module 3: Model Evaluation and Performance Analysis
  • NLP evaluation metrics (accuracy, precision, recall, F1-score, BLEU, ROUGE)
  • Comparative analysis of traditional ML vs. transformer-based models
  • Error analysis and bias considerations in NLP models
  • Module 4: Building an End-to-End NLP Pipeline
  • Pipeline architecture: preprocessing → modeling → evaluation → deployment
  • Integration of NLP components into complete systems
  • Deployment strategies via APIs, web applications, and cloud services

Course Procedures
  • The course starts on September 29, 2025. All coursework must be completed by November 30, 2025, in order to earn the micro-credential badge. You will continue to have access to the course materials until January 1, 2026. The approximate time to complete this course is 16 hours.
  • This course has an instructional period from September 29 to October 26, 2025. During this instructional period, course materials will be released weekly, and live synchronous sessions will be held. You may complete the course materials at your own pace. Live Zoom meetings will be conducted for interactive coding sessions and answering any questions you have.
  • You will receive the micro-credential badge upon successful completion of the course assessments.
  • Course Materials:
  • Course materials are provided within the course. No additional purchase is required.

Registration

Students

Register for the course as a 1-credit independent study course with a maximum of 3 such TrAC courses per semester.

Students must register for the independent study by emailing benearl@iastate.edu and cc-ing baditya@iastate.edu. Also, fill this Google form for our records: https://forms.gle/mQRefUJ4qpa29s5w6

Industry Professionals/ISU Staff/Post Docs

$ 500 .00
Register Now

ISU Professionals/Staff and Government Employees: $300

About the Instructor

Aditya Balu, Data Scientist

Aditya Balu is a data scientist in the Translational AI Center (TrAC) at Iowa State University. With 14 years of experience working in the field of AI, he brings extensive expertise in natural language processing and machine learning to this course. His rich experience includes developing and implementing NLP solutions for industry-level applications, making him uniquely qualified to guide students through the end-to-end pipeline for natural language processing. Aditya is passionate about bridging the gap between academic research and practical industry applications, ensuring students gain both theoretical knowledge and hands-on skills needed for real-world NLP challenges.