This module introduces the core concepts behind Natural Language Processing, including how computers process written and spoken language. Students explore tokenization, syntax, semantics, morphology and linguistic structures that form the foundation of AI language systems. Understanding these principles is essential for developing intelligent applications capable of interpreting human communication. For Caribbean businesses embracing digital transformation, these skills support chatbot development, customer engagement platforms and AI-powered automation.

Language representation focuses on how words, phrases and sentences are converted into mathematical formats that computers can understand. Students learn about embeddings, vector representations and semantic relationships that enable AI systems to capture meaning rather than simply recognizing words. These techniques underpin modern technologies such as ChatGPT, recommendation systems, translation engines and conversational AI assistants, making this one of the programme's most valuable technical modules.

Students discover how supervised and unsupervised machine learning algorithms are used to solve language-based problems. The module explores classification, prediction, language modelling and algorithm evaluation while demonstrating how AI systems improve through exposure to large datasets. These techniques form the backbone of intelligent automation, fraud detection, customer service automation and predictive analytics used across modern industries.

This module teaches students how AI organizes, searches and categorizes large collections of text data. Learners build an understanding of document classification, keyword extraction, search engines, sentiment analysis and recommendation systems. These capabilities are widely used by governments, banks, healthcare providers, tourism organizations and businesses to analyse customer feedback, monitor public opinion and improve decision-making using unstructured data.

The final module demonstrates how Natural Language Processing is used in today's AI landscape. Students examine applications including conversational AI, virtual assistants, machine translation, speech recognition, generative AI and Large Language Models (LLMs). They also consider ethical issues, performance evaluation and future developments in AI. This practical perspective prepares graduates to understand and contribute to the rapidly evolving field of artificial intelligence across numerous industries.