Natural Language Processing with Probability Models in Python

Understanding how computers process and predict human language is a core skill in modern technology. This course provides a clear, text-based path to mastering the mathematical and statistical foundations of Natural Language Processing using Python.\n\nYou will transition from understanding basic text preprocessing to building probabilistic models that can predict words, classify text, and analyze sequences. By reading through structured explanations and analyzing clean, modern Python code snippets, you will gain a practical intuition for NLP without needing a background in complex machine learning.\n\nWhat you'll learn:\n- Understand the core terminology and mathematical foundations of probability in NLP.\n- Build and evaluate N-gram language models for word prediction using modern Python type hints.\n- Implement Naive Bayes classifiers for sentiment analysis and text categorization.\n- Apply Hidden Markov Models and the Viterbi algorithm for part-of-speech tagging.\n- Practice data preprocessing, tokenization, and text normalization using industry-standard libraries.\n- Analyze model performance using modern evaluation metrics and validation techniques.\n\nThe course starts with foundational concepts of text processing and probability theory before moving step-by-step through N-grams, classification models, and sequential tagging algorithms. You will learn by reading detailed conceptual breakdowns paired with clean, executable Python code examples.\n\nThis course is designed for beginners who want to learn the mathematical foundations of NLP. No prior experience with natural language processing is required, though a basic familiarity with Python is helpful.\n\nStart your journey into language modeling and master the probability foundations of NLP today.