Natural Language Processing (NLP)

This course provides an in-depth exploration of Natural Language Processing (NLP), a field at the intersection of computer science, linguistics, and artificial intelligence that focuses on the interaction between computers and human languages. Students will learn the fundamental concepts and techniques used to process and analyze text data, including tokenization, stemming, lemmatization, and syntactic parsing. The course covers key NLP applications such as sentiment analysis, machine translation, text classification, and named entity recognition. Students will also explore advanced topics like word embeddings, sequence models, and transformer architectures, with hands-on experience using popular NLP libraries such as NLTK, SpaCy, and Hugging Face’s Transformers. By the end of the course, students will be equipped to build and deploy NLP models to tackle real-world language processing challenges. A background in machine learning and Python programming is recommended.