Introduction to Word Embeddings and Vector Space Models

In the world of artificial intelligence, computers do not read text the way humans do; they process numbers. Transforming raw words into meaningful mathematical vectors is the foundational step for all modern language technologies. This text-based course guides you through the core concepts of word embeddings, explaining how machines capture semantic relationships and context. You will start with the fundamental terminology of vector spaces and representation learning before moving into practical implementation patterns. Through clear explanations and structured code snippets, you will build a solid intuition for how words are mapped to continuous vector spaces. What you'll learn: Understand the transition from traditional bag-of-words models to dense word vectors; Apply popular embedding models to capture semantic similarity between words; Configure basic vector representations using Python and modern numerical libraries; Explore how modern transformer-based models generate context-aware embeddings; Practice measuring vector distance and similarity using cosine similarity metrics. The course begins with essential linguistic and mathematical foundations, ensuring you understand the 'why' behind vector spaces before exploring modern implementation standards. This course is designed entirely for beginners with basic Python familiarity and no prior background in natural language processing. Start your journey into language technology today and master the core mechanics of text representation.