Machines are trained to automatically categorize text into predefined categories or classes through a process called text classification, which enables them to understand and process human language in a way that approximates human-like understanding. The main difference between human reading and machine learning is that humans naturally understand meaning while machines rely on patterns and probabilities. To teach machines to read and classify text, they first break down the text into smaller pieces, convert words into numbers using methods like "one-hot encoding" or "word embeddings," and then learn to recognize patterns in these numerical representations. Machines use a combination of word order, context, relationships, and mathematical calculations to make classification decisions. The goal is to create systems that can understand and process human language effectively.