Natural Language Processing: An Introduction - Module MN5001 Programme content

During this module, you will

• Learn how to process and analyse text data using key NLP methods such as tokenisation, stemming, lemmatisation, and sentence segmentation.
• Build foundational knowledge of string similarity, n-gram language models, and spelling correction using tools like regular expressions and statistical modelling.
• Apply core NLP techniques to classify text and analyse sentiment, using tools like Naïve Bayes classifiers and sentiment lexicons.
• Explore NLP libraries and cloud platforms including NLTK, spaCy, TextBlob to understand real-world applications.

Key information

This micro-credential will be delivered online over 6 weeks.

You will be assessed using continuous skill-based assignments with no final exam.

Part-time considerations    

• Designed for working professionals    
• Evening or recorded lectures    
• Additional prep and group work time    
• Timetable provided after registration 

Module prerequisite

We use the Python programming language in this course. You do not need to be a professional Python programmer, but you do need to be familiar with the general programming concepts and their implementation in Python, examples of these include:

• Data types, variables, numbers, strings
• Numeral Operators, Booleans
• Data structures, lists, tuples, sets, dictionaries, arrays
• If…else statements
• While loops, For loops
• Functions
• Classes and objects

If you do have some coding experience in Python and are familiar with these topics then this course could be a good fit for you. Otherwise, learning Python and NLP at the same time could be too challenging.