What the course covers.
- Module 1
NLP Foundations & Text Preprocessing
Learners begin by understanding the core principles of natural language processing. Topics include tokenisation, stemming, lemmatisation, stopword removal, and vectorisation techniques such as TF-IDF and word embeddings. Hands-on exercises focus on preparing real-world datasets for NLP models, ensuring learners can clean, structure, and represent text data effectively.
- Module 2
Text Modelling & AI Applications
This module covers building AI models for a variety of NLP tasks, including text classification, sentiment analysis, summarisation, and named entity recognition (NER). Learners work with popular frameworks such as Scikit-Learn, SpaCy, and Hugging Face Transformers to implement models on real datasets and gain practical experience in solving real-world NLP problems.
- Module 3
Deployment & Advanced NLP Techniques
Learners explore how to deploy NLP systems in production environments using APIs, cloud platforms, and microservices. The module also covers advanced techniques such as embeddings for semantic search, transfer learning with LLMs, and fine-tuning pre-trained models. Participants gain the skills to evaluate model performance, optimise systems, and ensure reliability and scalability in real-world applications.
