This academic text provides a comprehensive overview of Natural Language Processing (NLP), starting with fundamental methods rooted in machine learning and optimization, such as Naïve Bayes and support vector machines for linear text classification. The material progresses through canonical NLP tasks, detailing the use of models like Hidden Markov Models for sequence labeling and exploring syntactic structures through formal language theory and various methods of dependency and context-free parsing.