Foundations of Natural Language Processing and Computational Linguistics

Human language is complex, unstructured, and filled with nuance, making it one of the most challenging data types for computers to process. This text-based course bridges the gap between linguistic theory and computational execution, taking you from foundational concepts to modern machine learning applications. You will learn how to transform raw text into structured data that algorithms can understand, analyze, and generate. By working through clear explanations and structured written exercises, you will develop a deep mental model of how computer systems parse, translate, and summarize human speech and text. You will progress from basic tokenization and part-of-speech tagging to sophisticated semantic analysis and modern retrieval-augmented generation patterns. What you'll learn: - Understand the core principles of computational linguistics, syntax, and discourse analysis. - Build statistical and machine learning models to analyze text corpora and extract key information. - Implement syntactic parsing algorithms to map sentence structures and relationships. - Apply probability models to statistical machine translation and text summarization workflows. - Explore modern NLP patterns including vector databases, embeddings, and prompt engineering basics. - Design foundational components for dialogue systems and structured information extraction. The course begins with essential terminology, linguistic rules, and text preprocessing techniques. From there, you will explore syntactic parsing, semantic representation, and statistical translation, concluding with an introduction to modern vector-based architectures. This course is designed for beginners who want a structured, academic-grade introduction to natural language processing without requiring prior background in computational linguistics. Start reading today to unlock the power of language technology.