Foundations of Word Embeddings in NLP

Natural Language Processing (NLP) empowers machines to understand, interpret, and generate human language, and word embeddings are at its core. This course will guide you through the essential principles of word embeddings, enabling you to transform text data into meaningful numerical representations for various NLP applications. What you'll learn: * Understand the fundamental concept of word embeddings and their role in NLP. * Learn how words are transformed into numerical vector representations. * Explore the mechanisms behind popular static embedding models like Word2Vec and GloVe. * Practice interpreting semantic relationships and analogies within vector spaces. * Grasp the conceptual differences and advantages of contextual embeddings in modern NLP. * Identify and understand the implications of bias in embedding models. The course begins with foundational definitions and the intuition behind vector representations, then progresses to classic and modern embedding techniques, concluding with practical considerations. This course is designed for beginners interested in natural language processing who want to understand how text is prepared for machine learning, with no prior NLP experience needed. Begin your journey to effectively represent and process human language with machines.