NLP Data Prep: Cleaning and Text Preprocessing in Python

Raw text data is rarely ready for machine learning models. Before you can build powerful natural language processing systems, you must learn how to clean, structure, and preprocess text effectively. This text-based course guides you through the foundational concepts and practical code patterns needed to transform messy, real-world text into high-quality input for NLP models. You will read clear explanations, study structured code snippets, and work through written exercises to build confidence in data preparation. What you'll learn: - Understand the core pipeline of text preprocessing and tokenization. - Clean noisy text by removing HTML tags, emojis, and unwanted formatting using regular expressions. - Apply normalization techniques including lowercasing, lemmatization, and stop-word removal. - Prepare text data for modern large language models and traditional NLP algorithms. - Use Python libraries to streamline your preprocessing workflows. - Structure raw text from diverse sources into clean datasets ready for analysis. The course begins with foundational definitions of text structures before moving into hands-on regular expressions and advanced normalization techniques. You will progress from raw string manipulation to fully structured data pipelines. Designed for beginners to NLP who have a basic understanding of Python programming, this course requires no prior data science experience. Start mastering the essential first step of any successful natural language processing project today.