What type of algorithms does Amazon SageMaker provide out of the box?

Amazon SageMaker offers a wide array of built-in algorithms that cater to different types of machine learning tasks. This includes both supervised learning and unsupervised learning algorithms.

Supervised learning algorithms are used for tasks where the model is trained on labeled data, which means the input data has corresponding output labels. Examples include classification and regression tasks. On the other hand, unsupervised learning algorithms are utilized when the model is trained on data without labeled responses, allowing it to identify hidden patterns or groupings in the data, such as clustering and dimensionality reduction.

The availability of both types of algorithms makes SageMaker a versatile tool for data scientists and developers, enabling them to address a variety of use cases without the necessity to build their algorithms from scratch. This flexibility is a significant advantage of the platform, facilitating the efficient development and deployment of machine learning models.

While custom algorithms can certainly be developed and used within SageMaker, the question specifically pertains to the pre-built options available, emphasizing the dual capability for supervised and unsupervised learning.