Text Tokenization and Vocabulary Building in NLP with TensorFlow

Processing raw text into a format that machine learning models can understand is the vital first step of any natural language processing workflow. This text-only course guides you through the fundamental mechanics of text preprocessing, showing you how to transform unstructured words into structured numerical data. You will start with the foundational concepts of vocabulary building, character versus word tokenization, and text normalization, before moving on to hands-on implementation. By completing this course, you will be able to confidently prepare text datasets for downstream deep learning models, ensuring your data pipelines are clean, efficient, and robust. What you'll learn: - Understand the core principles of tokenization and vocabulary construction in NLP - Clean and normalize raw text data to remove noise and unwanted characters - Implement TensorFlow's Tokenizer to map text corpora to numerical sequences - Manage out-of-vocabulary tokens and apply padding to handle variable-length inputs - Apply subword tokenization concepts to modern transformer-based workflows - Build reusable input pipelines for text classification and sequence modeling The course begins with essential terminology and the theory behind tokenization before guiding you through step-by-step written tutorials using TensorFlow's preprocessing tools. This course is designed for beginners in NLP and machine learning who have a basic familiarity with Python. No prior experience with natural language processing is required. Start your journey into NLP data preparation today.