Module Code: CT5182

Micro-credential Overview

The first half of this module will introduce the concept of Machine Learning and look at some interesting applications. The theoretical aspects of the subcategories of Machine Learning (Supervised Learning, Unsupervised Learning, Semi-supervised Learning, Reinforcement Learning, and Deep Learning) will be studied in detail as well as common terminology associated with each. The second half will apply these machine-learning techniques to problems in natural language, looking at the problems of text classification, annotation, translation, and knowledge extraction. In addition, we will cover some useful linguistic fundamentals for understanding the challenges of natural language processing.

Start Date

Academic Year 2024/25 - Semester 1

Mode of Study and Assessment

The mode of study for this module is Online Learning.

NFQ Level and ECTs

Level 9

5 ECTs 

Application Process

From the 1st April 2024 you can apply for any University of Galway Micro-credential through our Online Application Portal

When completing your application please make sure to select the following categories:

Academic level: Micro-credentials and CPD
College/Interest type: Micro-credentials/CPD Postgrad Level 9
Academic programme: Software Engineering & Database Technologies Micro-credentials – CPC1

You will need to manually enter the Module Name and Code under ‘Module Name’ on the second page of your application. Please copy the name and code below:

Machine Learning & Natural Language Processing - CT5182

You can find more details on our application process here.

Course Fees 


Subsided fee: €130

*Candidates who meet the eligibility criteria may qualify for a 80% fee subsidy, subject to the availability of subsidised places. For eligibility details, please refer to the Eligibility Criteria on the Micro-credentials webpage.

Entry Requirements 

  • Applicants must be over 21 years of age. A Level 8 undergraduate or Level 7 qualification with 3 years' experience is required. Applications can be considered using Recognition of Prior Learning.

Contact Information

If you have any queries please email: