Foundations of Word Vectors and Semantic Similarity

Words carry rich meanings, but computers only understand numbers. Discover how modern Natural Language Processing (NLP) bridges this gap by transforming text into dense mathematical vectors that capture real-world context. This course guides you from the absolute basics of language representation to calculating semantic similarity between words, phrases, and documents. You will understand how machines grasp meaning, identify synonyms, and power modern search and recommendation systems. What you'll learn: - Learn the core concepts of vector spaces and word embeddings - Calculate semantic similarity using metrics like cosine similarity - Understand classic representation models including Word2Vec and GloVe - Explore modern contextual embeddings and transformer-based representations - Practice implementing vector operations using popular Python libraries - Discover how vector databases store and retrieve semantic information for modern applications Starting with fundamental terminology and mathematical intuition, the course transitions into practical, step-by-step applications. You will work through clear written explanations, code walkthroughs, and conceptual exercises to build a solid NLP foundation. This course is designed for beginners interested in data science, artificial intelligence, and text analysis. No prior NLP experience is required, though basic Python familiarity is helpful. Start reading today to unlock the power of semantic text analysis.