Natural Language Processing (NLP)

Description

The course covers foundational concepts such as tokenization, part-of-speech tagging, syntactic parsing, and semantic analysis, providing students with a solid understanding of how computers process and interpret text. Participants also delve into practical applications of NLP, including sentiment analysis, named entity recognition, machine translation, and text summarization, learning to leverage state-of-the-art tools and libraries such as NLTK, spaCy, and Transformers. Through hands-on projects and exercises, students gain experience in preprocessing text data, building NLP models, and evaluating their performance, ultimately preparing them to tackle real-world challenges in areas like information retrieval, question answering, and conversational AI.