Loading...

Course Description

Save when you register for BIOF 395 and BIOF 495 as a bundle!

This bundle provides a comprehensive introduction to modern Natural Language Processing (NLP), text mining, and foundation models for analyzing and generating human language. Students will learn how to transform unstructured text into structured knowledge using contemporary NLP techniques, machine learning methods, and large language models. The courses cover the complete NLP workflow, including text preprocessing, representation, modeling, evaluation, and practical applications such as named entity recognition, topic modeling, text summarization, and downstream language understanding tasks. Students will gain hands-on experience using Python and widely adopted NLP libraries—including NLTK, spaCy, scikit-learn, Hugging Face, and GPT-based models—to build, evaluate, and deploy modern language processing solutions. The course also explores the architecture and practical use of encoder-based models such as BERT and decoder-based models such as GPT, enabling students to understand their capabilities, limitations, and appropriate use across a range of real-world applications.

Learner Outcomes

When you complete these courses successfully, you will be able to:
  • Describe the fundamental concepts of natural language processing and text mining, including text preprocessing, representation, modeling, and evaluation techniques
  • Apply Python and widely used NLP libraries, including NLTK, spaCy, scikit-learn, and Hugging Face, to develop practical language processing applications
  • Compare and implement a range of NLP techniques, including named entity recognition, topic modeling, text summarization, and other downstream language processing tasks
  • Explain the fundamentals of foundation models, including the architectures and design principles of encoder-based and decoder-based language models
  • Apply decoder-based large language models, such as GPT, using prompting, in-context learning, and instruction-tuned approaches to solve practical NLP tasks
  • Apply encoder-based language models, such as BERT, to downstream NLP tasks and evaluate their strengths relative to decoder-based models
  • Evaluate the performance of traditional NLP models and large language models, considering accuracy, limitations, and practical deployment considerations
  • Understand how statistical machine learning and modern foundation models complement one another in solving real-world natural language processing problems

Microcredential(s)

These courses apply toward the Bioinformatics Endeavor and Artificial Intelligence and Machine Learning for Biomedical Researchers: Bioinformatics Specialization digital badges.

       

Textbook Information

A textbook is available for these courses. 
Click here to view a textbook list for FAES courses and purchasing information. Please note that tuition does not include textbooks.

Prerequisites

Prior exposure to programming and Python is highly recommended.

Refund
Follow the link to review FAES Tuition Refund Policy.

Scholarship and Funding

Are you a self-funded student? FAES offers scholarship options. Click here for more information and to apply. 

Looking for resources to help you acquire funding for your continued education? Click here for our funding justification guide. 

Photo Release

By registering for this event, you agree to allow FAES to take photographs of you during the event and to use these photos for promotional purposes, including on our website, social media, and marketing materials, without further compensation. You understand that you have no right to review or approve the final use of these images.

Loading...

To Register Click on "Add to Cart"

Section Title
2-COURSE BUNDLE - Introduction to Text Mining and Large Language Models
Type
Online Asynchronous
Dates
Aug 26, 2026 to Dec 08, 2026
Total Cost (Includes $100 non-refundable technology fee per course when applicable)
Eligible Discounts Can Be Applied at Checkout (4 Credits) $1,460.00
Potential Discount(s)
Available for Academic Credit
4 Credit(s)
Required fields are indicated by .