How does machine learning (ML) primarily differ from traditional data analysis?

Machine learning primarily differs from traditional data analysis in that it leverages examples from large amounts of data to learn patterns and make predictions or decisions. This approach allows machine learning algorithms to identify complex relationships within the data that may not be immediately visible through traditional analysis methods.

In traditional data analysis, insights are typically derived from pre-defined queries or hypotheses applied to structured datasets. In contrast, machine learning systems can analyze unstructured and structured data at scale, discovering patterns through training on diverse data points without the need for explicit programming for every task. This ability to learn from examples and improve over time is what sets machine learning apart, enabling more sophisticated and automated analysis.

The use of large datasets is crucial in machine learning because it enhances the model's ability to generalize from the training data to unseen data, which is essential for tasks such as classification, regression, and clustering. This makes option C the most accurate distinction between machine learning and traditional data analysis methods.