Natural Language Processing

With the growing amount of data in digital form, skills in NLP have become crucial for businesses to extract insights from written and spoken human language. For example, Generative AI, such as ChatGPT, is causing a stir in industry, both in its challenges and opportunities.

Our Professional Diploma in Natural Language Processing is for those who want to deep dive into this specialist field for the expansive career opportunities it presents. You could be a graduate of a Computer Science or IT programme who wants to specialise in this space; or a software developer, data analyst, or language specialist who wants to upskill in the field of NLP. It's also for engineers who want to learn the latest deep learning and natural language processing technology to benefit their businesses.

This UL@Work programme is co-designed with industry to ensure skills transfers are industry-responsive for current and future needs. Our programmes are developed for working professionals and are designed to be flexible and accessible. You can take it online, at your own pace, and from anywhere worldwide.

On this NFQ Level 9 programme, you'll explore natural language processing frameworks to understand their core features and usability. You'll design code to implement solutions to a range of NLP-related problems in your workplace and learn how to use the right technologies, frameworks, and platforms to build natural language processing solutions that work well.

NLP skills are in high demand right across. For example, businesses need NLP to understand customer feedback, monitor brand reputation, and analyze social media interactions. In healthcare, NLP can be used to extract insights from electronic health records and medical literature.

The skills you'll acquire from this Professional Diploma in NLP are transferable to various job roles. Here are a few examples:

Data Scientist - In this role, you'll use NLP to extract insights from text data and build predictive models. You'll work with large datasets and use statistical methods to find patterns in the data.

Machine Learning Engineer - As a machine learning engineer, you'll work on developing algorithms that enable machines to learn from text data. You'll collaborate with data scientists and software engineers to build and deploy NLP models.

Computational Linguist - In this role, you'll work on developing and improving NLP algorithms. You'll use your understanding of human language to improve the accuracy of NLP models and make them more efficient.

Award: Professional Diploma
Qualification: NFQ Level 9 Minor Award

Semester 1

Introduction to Natural Language Processing:
This module introduces you to the world of Natural Language Processing (NLP). We cover the fundamentals of statistical NLP and its techniques and applications with a foundational approach.

Information Retrieval
This module offers an overview of the fields of Information Retrieval, Information Extraction, and Semantic Web. The module will cover a blend of fundamental concepts and current tools, techniques, and technologies used in modern information retrieval systems.

Semester 2

Advanced Natural Language Processing
This module covers advanced-level topics in natural language processing, focusing on deep learning-based approaches. These include text classification, synthetic parsing, part-of-speech tagging, named-entity recognition, coreference resolution, and machine translation. You will be taken through neural network architectures, including convolutional neural networks, recurrent neural networks, to long short-term memory networks (LSTMs).

Natural Language Understanding:
This module explores the field of Natural Language Understanding and related topics, including sentiment analysis, relation extraction, natural language inference, semantic parsing, question answering, language generation, and large language models like ChatGPT and conversational agents.

Both Semesters

Future Focused Professional Portfolio 1 & 2:
In the first module, you will be led through a series of talks about the future of technology, the future of markets, and the future of society as a whole. You'll work collaboratively to identify key trends impacting your role and organisation. You'll also build a professional network and use it to reach out to key thought leaders in this area.

The second module will provide you with an opportunity to demonstrate independent and self-determined learning through the creation of your own individual portfolio. Your portfolio includes various activities that will show how you've improved your reflective practice, how well you've used discipline-specific knowledge in different situations, and how you've led a discussion about the future of your field.

The principal entry requirement is a Level 8 honours degree, at minimum second class honours (NFQ or other internationally recognised equivalent), in a relevant engineering, computing, mathematics, science or technology discipline.

Alternative Entry Route:

In accordance with the University's policy on the Recognition of Prior Learning, candidates who do not meet the minimum entry criteria may be considered. Applicants from other disciplines who have a relevant mathematics and computing element in their primary degree will also be considered. Applicants who possess an honours u/g degree, minimum second-class, or equivalent in a non-numerate discipline and have a minimum of 3 years experiential learning in an appropriate computing discipline will be considered. English Language Requirements, right to shortlist, interview, and RPL policy will apply.

What to include with your application:

Qualification transcripts and certificates

A copy of your birth certificate or passport

A copy of your CV

If your qualifications have been obtained in a country where English is an official language this will suffice

If this is not available, the following additional documents must be provided:

• English translation of your qualification(s)/transcripts
AND
• English language competency certificate

1 year part-time