Text Processing and Machine Learning: Classification & Duplicate Detection

Unstructured text data is everywhere, from customer feedback and support tickets to product descriptions and news feeds. Extracting valuable insight from text requires clear, specialized machine learning workflows and text processing techniques. In this practical written course, you will develop the skills to transform raw text into structured features, categorize documents accurately, group related content, and identify near-duplicate records. You will begin by learning fundamental text processing concepts and normalization steps before progressing to supervised classification, unsupervised clustering, and modern duplicate detection methods. What you'll learn: - Understand essential natural language processing terminology, tokenization, and text cleaning fundamentals. - Convert text into numerical representations using TF-IDF and modern vector embeddings. - Train machine learning algorithms to perform reliable document classification. - Apply clustering techniques to automatically discover topics and patterns in text collections. - Implement fuzzy matching and locality-sensitive hashing to detect near-duplicate texts efficiently. - Evaluate model performance using standard metrics for text processing tasks. Starting with key definitions and text preprocessing concepts, the material guides you step by step through feature extraction, classification workflows, clustering strategies, and duplicate detection techniques. Designed for beginner data analysts and developers, this course requires no prior natural language processing experience. Begin reading today to build practical text analysis skills.