N-Grams and Language Models for Beginners

Language models and text analysis systems rely on understanding how words and characters sequence together. This text-only course guides you through the foundational concepts of n-grams, the building blocks of modern natural language processing. You will transition from knowing nothing about text probability to confidently analyzing, calculating, and applying sequence models to solve real-world text processing challenges. What you'll learn: Understand the core mathematical and linguistic concepts behind unigrams, bigrams, and trigrams; Calculate sequence probabilities using maximum likelihood estimation and smoothing techniques; Apply n-gram models to practical tasks like text prediction, spelling correction, and language identification; Compare classic n-gram modeling with modern tokenization methods used in large language models; Practice analyzing text data and evaluating model performance through written exercises. The course begins with essential terminology and the basic math of probability before moving on to practical text processing scenarios. You will progress through structured text explanations and self-assessment challenges designed to solidify your understanding. This course is designed for beginners in data science, computational linguistics, or software development who want to understand the mechanics of text prediction with no advanced prerequisites. Start reading today to master the core principles of text sequencing.