In the realm of machine learning, the creation of models can be broadly categorized into supervised and unsupervised learning.

Supervised learning is a method where a model is trained on labeled data, meaning the algorithm is provided with input-output pairs, and it learns to map the inputs to the desired outputs. Examples include regression, where we predict a continuous value, and classification, where we assign input data into one of the predefined categories.

On the other hand, unsupervised learning operates without labeled data, aiming to identify patterns or structures within the data. The algorithm isn't told the "correct" answer but rather tries to extract insights on its own. Techniques such as clustering, where data is grouped based on inherent similarities, and dimensionality reduction, where redundant or less informative features are minimized or removed, are classic examples.

Both supervised and unsupervised learning methods are fundamental in data science and offer various tools to address a wide range of problems and challenges.