How does machine learning differ from traditional programming?

Machine learning fundamentally differs from traditional programming in that it learns from data to make predictions rather than relying on predefined rules. In traditional programming, developers write explicit instructions that dictate the behavior of the program, defining how inputs are transformed into outputs through a set series of rules. This approach can be rigid, as it requires the programmer to anticipate all possible scenarios and conditions that the software may encounter.

In contrast, machine learning utilizes algorithms that can analyze patterns and relationships in data. Instead of following a strict set of predefined instructions, these algorithms improve their performance as they are exposed to more data, allowing them to adapt and make predictions based on patterns it has learned. This ability to learn and evolve over time is what enables machine learning to handle more complex tasks and scenarios than traditional programming might allow.