Foundations of Word Vectors and Semantic Similarity

Natural Language Processing relies on turning human language into numbers that computers can understand. This text-only course guides you from the foundational concepts of word embeddings to calculating semantic similarity in your own projects. You will start with key terminology and basic concepts before moving into practical applications. What you will learn: Learn the core concepts of word embeddings and vector space models. Compare traditional representation methods like One-Hot Encoding with modern dense word vectors. Calculate semantic similarity using Cosine Similarity. Explore pre-trained models such as Word2Vec, GloVe, and FastText. Understand how modern vector databases store and retrieve embeddings. Implement basic semantic matching workflows using Python. This course begins with essential terminology and mathematical intuition, progressing step-by-step to code-based examples. It is designed for beginners in data science, AI, or software development, with no prior machine learning experience required. Start reading today to unlock the power of semantic understanding in your applications.