NLP Foundations: Classification and Vector Spaces

Natural Language Processing transforms how we interact with technology by turning human language into data that machines can understand. This course provides a clear path for beginners to master the essential techniques used to categorize text and map the relationships between words. You will transition from understanding basic linguistics to building functional models that can detect sentiment and translate between languages. By the end of this course, you will be able to represent words as mathematical vectors and use those representations to solve real-world text analysis problems. What you'll learn: - Understand the fundamental terminology of Natural Language Processing and vector mathematics - Apply Logistic Regression and Naive Bayes to perform sentiment analysis on text data - Use vector space models to identify semantic similarities and relationships between words - Practice dimensionality reduction using Principal Component Analysis to simplify complex text data - Explore modern word embedding patterns used in current large language models - Implement approximate k-nearest neighbors and locality-sensitive hashing for efficient text retrieval The course begins with foundational definitions and the mechanics of text preprocessing before moving into practical classification algorithms and vector space theory. You will read detailed explanations and apply your knowledge through written coding exercises focused on core NLP logic. This course is designed for beginners interested in data science or machine learning. No prior experience with NLP is required. Start your journey into the world of language technology today.