Machine learning algorithms differ from traditional programming in their approach to problem-solving. In traditional programming, a developer writes explicit instructions to solve a problem using predefined rules and logic. The input data is processed by these rules to produce the output.

In contrast, machine learning algorithms learn from data. Instead of being explicitly programmed, they are trained on a dataset, identifying patterns and making predictions or decisions based on this training. The algorithm adjusts itself based on the data it processes, allowing it to improve over time without explicit reprogramming. This approach is particularly effective for complex tasks like image recognition, natural language processing, and predictive analytics, where writing explicit rules is impractical or impossible.

Overall, traditional programming relies on human-crafted rules, while machine learning leverages data-driven learning to derive insights and make decisions.