This Natural Language Processing (NLP) course offers a foundational and practical understanding of key NLP techniques, from text processing and feature extraction to modern machine learning and deep learning approaches. Students will explore core methods like tokenization, sentiment analysis, and topic modeling, using tools such as NLTK and spaCy. The course delves into advanced models, including RNNs, LSTMs, and Transformers like BERT and GPT, highlighting their real-world applications. Through hands-on projects, students will learn to build and evaluate NLP models, understand ethical considerations, and apply NLP techniques across various industries, preparing them for advanced work in AI-driven language processing.

Temps présentiel : 30 heures

Charge de travail étudiant : 70 heures

Méthode(s) d'évaluation : Examen final, Examen partiel, Travail personnel