What is 'machine learning'?

Machine learning is indeed best described as a subset of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed. This means that instead of being given specific instructions for every possible scenario, machine learning algorithms can analyze data and identify patterns to make decisions or predictions on their own.

This learning process typically involves training a model on a dataset, allowing the model to adjust its parameters based on the input data and the outcomes it needs to predict. As the system is exposed to more data over time, it enhances its performance, becoming more accurate without the need for further programming adjustments from developers.

The other options describe concepts that do not capture the essence of machine learning. While programming languages are essential for developing AI applications, they do not embody the learning aspect that is central to machine learning. Producing random data for testing purposes relates more to simulations or testing methodologies, which is not inherently linked to learning. Automated decision-making systems might utilize machine learning, but they do not define it, as many decision-making systems can operate based on predefined rules without the learning aspect.