What type of machine learning uses linear regression and logistic regression algorithms?

The correct choice, which identifies the type of machine learning that uses linear regression and logistic regression algorithms, is supervised learning. This is because supervised learning involves the use of labeled data to train models. In this context, algorithms like linear regression and logistic regression are employed to find relationships between input features and output labels.

Linear regression is used for predicting continuous outcomes, effectively modeling the relationship between one or more variables by fitting a linear equation to observed data. Logistic regression, on the other hand, is utilized for classification problems where the outcome is categorical, typically used to determine the probability of a data point belonging to a particular class.

Both these regression techniques are part of the broader category of supervised learning, which encompasses any modeling approach that relies on a training dataset that pairs input with known output. In contrast, clustering is a type of unsupervised learning focused on grouping similar data points without predefined labels, and data mining refers to the broader process of discovering patterns in large datasets rather than a specific type of learning algorithm.