Which statement correctly describes machine learning?

The statement that correctly describes machine learning is that it adapts based on learned data patterns. Machine learning is fundamentally about creating algorithms that can learn from and make predictions or decisions based on data. These algorithms identify patterns within data sets, allowing them to improve their performance over time as they are exposed to more examples. This ability to learn and adjust based on new inputs sets machine learning apart from traditional programming, where each task must be explicitly coded by humans.

The other statements are inaccurate representations of machine learning principles. For instance, manually programming for every task implies a static approach where no learning occurs, which contradicts the essence of how machine learning algorithms evolve through experience. Moreover, stating that machine learning does not require data fails to recognize that, without data, there is nothing for the algorithms to learn from or improve upon. Lastly, the notion that machine learning operates exclusively with structured data overlooks the fact that many machine learning techniques are designed to work with unstructured data, such as text, images, and audio, which are prevalent in real-world applications.