Data Preparation for Sentence Classification with CNNs

Preparing raw text for deep learning models is often the most critical step in building successful natural language processing applications. This course guides you through the essential pipeline of acquiring and structuring text data specifically for convolutional neural networks (CNNs) used in sentence classification. You will transition from handling messy, raw text files to generating clean, vectorized inputs ready for model training. What you'll learn: Understand the foundational concepts of text processing and how CNNs interpret sentence structure; Download and parse text datasets from public repositories; Implement modern tokenization and text normalization techniques; Manage vocabulary creation and map words to numerical indices; Apply padding and truncation to handle variable-length sentences; Structure processed data into efficient pipelines using modern Python tools. The course begins with core terminology and text-processing fundamentals before moving step-by-step through practical data engineering workflows, ensuring you understand the 'why' behind each preprocessing decision. This course is designed for beginners in natural language processing and developers looking to understand the data-prep pipeline for deep learning, with no advanced prerequisites required. Start mastering the art of text data preparation today.