Description

This course provides an introductory overview of Natural Language Processing (NLP) from a computer science perspective. Students will explore the main concepts, methods, and tools used to process, analyze, and model human language computationally, with emphasis on text preprocessing, linguistic foundations, text representation, and classical machine learning approaches.

The course also introduces modern NLP architectures, including neural networks and transformer-based models, and highlights their use in practical applications such as text classification, sentiment analysis, named entity recognition, and information extraction. Through lectures and hands-on activities, students will develop the ability to design, implement, and evaluate basic NLP solutions while understanding their limitations, ethical implications, and real-world relevance.

Recommended textbooks 

[1]Text Analytics with Python A Practitioner’s Guide to Natural Language Processing  
[2] Getting started with natural language processing