NLP Text Processing: Tokens and N-Grams

Are you looking to understand how computers process human language? This course introduces the essential building blocks of Natural Language Processing (NLP): tokens and n-grams. You will learn to transform raw text into structured data, a critical first step for any text analysis or machine learning project. By the end of this course, you will be able to confidently apply core text preprocessing techniques, preparing you to tackle more advanced NLP concepts. You'll develop a clear understanding of how textual information is represented and manipulated for computational tasks. What you'll learn: * Understand the fundamental role of tokens and n-grams in NLP pipelines * Apply various tokenization strategies to segment raw text data * Construct n-grams to capture sequential patterns and context in text * Implement basic text normalization techniques like stemming and lemmatization * Analyze the impact of different preprocessing choices on text features * Practice cleaning and preparing text for downstream NLP tasks * Recognize common challenges and best practices in text preprocessing workflows This course begins with core terminology and concepts, then progresses through practical applications of tokenization and n-gram creation, along with essential text normalization. You will read clear explanations and review code snippets to reinforce your understanding. This course is designed for absolute beginners with no prior NLP experience. All you need is a desire to learn how to prepare text for analysis. Start your journey into understanding text data today.