Natural Language Processing

Catalog description

Natural Language Processing offers an in-depth exploration of modern data-driven techniques for natural language processing (NLP). The course progresses from basic text preprocessing methods to advanced machine-learning (ML) models specifically tailored for NLP tasks. Topics include sentiment analysis, named entity recognition, machine translation, and the use of advanced neural network architectures such as recurrent neural networks (RNNs), long short-term memory (LSTM), and transformers. Industry-standard tools and frameworks are covered for practical applications and project-based learning. For successful completion of this course, learners should take AI and ML Foundations, Machine Learning for Computer Scientists, and Deep Learning prior to this course.