Foundations of NLP: Probabilistic Models and Word Embeddings

How do search engines predict your next word, and how do spelling correctors know what you meant to type? Natural language processing relies on elegant probabilistic models to make sense of human language. By understanding these core mathematical frameworks, you unlock the ability to analyze, predict, and represent text computationally. This course guides you through the foundational mathematical and algorithmic concepts of NLP. You will transition from understanding basic text probabilities to implementing core algorithms that power modern language technologies, establishing a rock-solid base for advanced AI and machine learning. What you'll learn: - Understand the fundamental probability theories and tokenization strategies, including modern subword tokenization, that underpin computational linguistics. - Build an auto-correct system using minimum edit distance and dynamic programming. - Apply the Viterbi algorithm and Hidden Markov Models for accurate part-of-speech tagging. - Develop N-gram language models to predict subsequent words and implement auto-complete features. - Create word embeddings using the Word2Vec continuous bag-of-words model to capture semantic meaning. - Explore how classical probabilistic models transition into modern transformer-based vector representations. You will start with essential terminology and probability basics before moving step-by-step through text processing, sequence labeling, and neural word representations. Each concept is reinforced with clear written explanations and practical Python code snippets designed for hands-on learning. This text-only course is designed for beginner programmers, data enthusiasts, and aspiring machine learning engineers who want to understand the inner workings of NLP. No advanced machine learning background is required. Begin your journey into the mathematics and algorithms of language processing today.