AI 2—Natural Language Processing

Natural Language Processing (NLP) is a key interdisciplinary field that connects computer science, linguistics, and artificial intelligence, focusing on enabling computers to interact with human language effectively. This course provides a comprehensive overview of NLP, covering its definition, development, applications, and core learning content. Below is a detailed overview of the course, followed by a structured table summarizing key modules and their content.

NLP has evolved from early rule-based analysis to advanced large language models, becoming an indispensable part of daily life and AI research. This course is designed for learners interested in NLP, combining theoretical knowledge with practical skills to help them master the fundamentals and applications of NLP.

Core Module	Key Content
Definition of NLP	An interdisciplinary field integrating computer science, linguistics, and AI. Its core goal is to enable computers to understand, interpret, generate, and interact with human language meaningfully and usefully.
Significance of NLP	Language is the primary medium of human communication, carrying rich information, emotions, and intentions. NLP solves the challenge of converting unstructured language data into machine-processable format, becoming a core part of AI research.
NLP Evolution	From early rule-based language analysis to the era of large language models (LLMs) like GPT, BERT, and T5, NLP has evolved from a niche field to an integral part of daily life.
Daily NLP Applications	Virtual assistants (Siri, Alexa), machine translation tools, sentiment analysis for business feedback, and 24/7 chatbot support—all rely on NLP technologies.
Course Objectives	Guide learners through NLP fundamentals and advanced concepts, combining theoretical principles with practical application using popular tools and frameworks.
Course Content Outline	1. Foundational skills: Text preprocessing, tokenization, part-of-speech tagging 2. Advanced topics: Text understanding, sentiment analysis, machine translation, prompt engineering
Target Audience	Computer science students, software developers seeking skill expansion, and anyone curious about how machines "understand" human language.
Learning Outcomes	1. Design and implement simple NLP applications 2. Understand the capabilities and limitations of state-of-the-art NLP models 3. Recognize ethical and practical considerations in NLP development and deployment
Course Opening	Embark on the journey to unlock the power of human language and build intelligent systems that bridge the gap between humans and machines.