Course Description

Text mining, often referred to as Natural Language Processing (NLP), is a swiftly expanding field spurred by the progress of Artificial Intelligence (AI). Its primary objective revolves around converting unstructured text into structured knowledge, thereby streamlining data curation and enabling knowledge discovery. The volume of textual data has been growing rapidly. The amount of popular text databases is at a million or trillion scales. As exemplified by the COVID-19 pandemic, LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/) has meticulously tracked around 370,000 articles pertaining to COVID-19, with a monthly influx of approximately 10,000 articles. In such scenarios, text-mining techniques prove indispensable. For instance, the automatic extraction of symptoms, diseases, and drugs mentioned in these articles could assist in diagnosing and managing COVID-19 patients.

Learner Outcomes

When you complete the course successfully, you will be able to:

  • Describe text mining essentials, spanning text preprocessing, text representation methodologies, text modeling techniques, and evaluations
  • Utilize Python and popular libraries such as NLTK, spaCy, scikit-learn, and huggingface 
  • Compare range of cutting-edge text mining applications, including but not limited to named entity recognition, topic modeling, and text summarization, all of which hold immediate applicability across diverse domains
  • Gain an introduction to statistical models of Machine Learning applied to NLP and IR


This course applies toward the Bioinformatics Endeavor digital badge.

Textbook Information

There is no textbook for purchase required for this course.


Prior exposure to programming and Python is highly recommended, but not required to attend this class. We will provide a crash tutorial on Python in the first week. Learners without experience in programming languages are expected to practice and grasp the basic syntax of Python. There are also plenty of coding exercises to practice throughout the course. If you are unsure that you meet the prerequisite requirements, please contact registrar@faes.org and provide information about your course of interest and background knowledge.

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