Which of the following is true about machine learning?

Machine learning is fundamentally characterized by its ability to allow algorithms to learn from data. This process involves developing models that can analyze input data, identify patterns, and make decisions or predictions based on that information. Through this learning process, models can improve their performance over time as they are exposed to more data, which is a core principle of machine learning. This characteristic distinguishes it from traditional programming methods, where explicit instructions are coded into a program without the ability to learn from new data.

In contrast, the other options do not accurately reflect the principles of machine learning. For instance, machine learning is not limited to just statistical analysis; it encompasses a wide range of applications beyond statistics, including natural language processing, game playing, and predictive modeling. Additionally, machine learning relies heavily on data for training algorithms, contradicting the notion of programming without data. Lastly, while image recognition is a prominent application of machine learning, it is far from the only one, as the technology applies across diverse fields such as finance, healthcare, and autonomous systems.