What defines machine learning?

Machine learning is fundamentally characterized by its ability to enable machines to learn from data. This means that rather than following explicit programming instructions for every action, machine learning algorithms can identify patterns and make decisions based on the information they are exposed to. By learning from past experiences (the data), machines can adapt and improve their performance on a specific task over time without needing to be specifically programmed for each new scenario.

In the context of the other options, while manual data entry is an important process in data handling, it does not illustrate the concept of machine learning. The notion that machine learning only processes numerical data is inaccurate, as it can work with various data types, including text, images, and sounds, broadening its application far beyond mere numerical analysis. Lastly, the claim that machine learning relies solely on human input is misleading; although human expertise is essential in curating and preparing datasets, the learning process itself is automated and driven by the algorithms analyzing the data independently. Therefore, the second choice correctly captures the essence of machine learning.