Introduction Artificial Intelligence (AI) often feels like magic—systems that can recommend movies, recognize faces, or even write text. But behind the scenes, there’s no magic at all. AI models learn through a structured process that transforms raw data into meaningful decisions. Understanding this journey—from data to decisions—helps demystify AI and reveals both its strengths and limitations. 1. The Foundation: Data Collection Every AI model starts with data. Data is the raw material that fuels learning. This can include images, text, audio, numbers, or even user behaviour. For example, an AI trained to recognize cats needs thousands (or millions) of images labelled as “cat” or “not cat.” The more diverse and high-quality the dataset, the better the model can generalize. However, …
