How does Machine Learning acquire knowledge?

Machine learning acquires knowledge primarily through the process of finding relationships between input and output data during the training phase. This involves using large datasets to enable the algorithm to identify patterns and correlations. The algorithm learns from these examples, adjusting its parameters to minimize error and enhance accuracy in predicting outcomes. This ability to learn from data, rather than relying solely on predefined rules or manual inputs, allows machine learning systems to improve over time as they are exposed to more data and experiences.

The other methods suggested, such as storing data manually or following strict rules, do not encapsulate the essence of how machine learning models are designed to function. These alternatives lack the adaptive learning characteristic that is fundamental to machine learning, thus making them less applicable in the context of knowledge acquisition in this field.