How does machine learning differ from traditional programming?

Machine learning is distinct from traditional programming primarily in its ability to enable systems to learn from experience and data. In traditional programming, developers write explicit instructions to dictate how a program should behave in various scenarios. Each possible input and the corresponding output must be predetermined and coded manually.

In contrast, machine learning algorithms leverage large datasets to identify patterns and make decisions based on the information they process. This means that instead of being manually coded for every task, these systems improve over time as they are exposed to more data, effectively "learning" from past experiences to enhance accuracy and performance. This capability allows for greater flexibility and adaptability in handling complex tasks ranging from image recognition to natural language processing.

This concept is what sets machine learning apart as it moves beyond static functions, instead relying on dynamic data-driven insights to function, which highlights its broad application opportunities in modern technology.