Natural Language Processing with N-Grams and Text Classification

Text data is highly unstructured, making it difficult for standard machine learning algorithms to process. To unlock the meaning behind words, you need to structure text using NLP techniques like N-grams. This text-only course guides you through the process of converting raw text into meaningful numerical features that algorithms can understand. You will start by learning core NLP terminology and foundational preprocessing concepts, including tokenization and handling stop words. From there, you will explore how grouping adjacent words into bigrams and trigrams captures context that single words miss. You will practice configuring CountVectorizer in Python and applying these structured features to train text classification models. What you will learn: - Understand core NLP terminology and text preprocessing fundamentals - Configure CountVectorizer to extract unigrams, bigrams, and trigrams - Clean and normalize raw text using Python libraries - Build structured feature matrices for machine learning algorithms - Apply N-gram models to solve real-world text classification tasks - Evaluate model performance using standard classification metrics This course is designed for beginners who want to build a solid foundation in natural language processing. No prior NLP experience is required, though a basic familiarity with Python programming will help you get the most out of the written examples and exercises. Start your journey into natural language processing and master text classification today.