Machine learning serves as a foundational tool for understanding data and making predictions, while artificial intelligence builds on these insights to enhance decision-making. A practical example is spam detection in email systems, where user feedback helps train models to classify emails as spam or legitimate, revealing distinct patterns in language and structure. This interplay between user input and machine learning algorithms showcases the power of supervised classification in real-world applications.
