NLP Series Day 2: How Tokenization and Normalization Prepare Text for AI

Text preprocessing is a foundational step in natural language processing (NLP) that cleans and organizes raw human language before it can be analyzed by algorithms. Tokenization involves breaking text into smaller units called tokens — such as words, characters, or subwords — so that NLP models can identify patterns and relationships between them. Normalization standardizes inconsistent text by applying steps like lowercasing, removing punctuation, and stripping extra whitespace, ensuring that variations like 'Dog,' 'DOG,' and 'dog!' are treated as the same word. Together, these two techniques make text more structured and predictable for downstream machine learning tasks. The lesson also demonstrates how both processes can be implemented in Python using only the standard library, without any external dependencies.