Language Processing in Practice

(3 Credit Hours) · Undergraduate

Natural Language Processing (NLP) has emerged as a crucial skill in the workforce, especially with the advent and accessibility of generative AI technologies. From intelligent chatbots and virtual assistants to automated content creation and sentiment analysis, NLP applications are transforming industries and redefining how we interact with technology. Mastery of NLP techniques and tools not only opens doors to careers in the technology sector but also equips students to contribute to innovations that shape our future. Language Processing in Practice is a hands-on course designed to introduce students to the fundamental theory and applications of NLP, with a special emphasis on working with large language models, generative AI, and the Hugging Face ecosystem. The course focuses on practical techniques for processing, analyzing, generating, and understanding human language data. Students will explore key topics such as text preprocessing, tokenization, part-of-speech tagging, parsing, sentiment analysis, topic modeling, machine translation, and text generation. The curriculum places a strong emphasis on modern NLP libraries and frameworks like NLTK, spaCy, and particularly Hugging Face Transformers. Through a series of projects and assignments, students will gain experience in building NLP applications, creating word embeddings with pre-trained large language models, and generating human-like text using generative AI models. Basic proficiency in Python programming is required.