Ace the AI Engineering Exam 2025 – Transform Your Tech Dreams into Reality!

In unsupervised learning, the primary goal is to find patterns and structures in unlabeled data. Unlike supervised learning, where the model is trained using labeled data (data that includes inputs paired with correct outputs), unsupervised learning deals with datasets that lack those explicit labels. This method allows the model to explore the data independently, making it well-suited for discovering hidden structures within the data.

The focus on identifying patterns includes clustering data into groups based on similarities, reducing the dimensionality of the data while retaining important features, and detecting anomalies. By doing so, unsupervised learning can reveal insights that may not be immediately apparent and can help inform further analysis or preprocessing steps for subsequent models.

The other options do not align with the objectives of unsupervised learning. For example, maximizing prediction accuracy and assigning labels are hallmarks of supervised learning, while calculating correlation coefficients is a statistical method to measure relationships between variables, rather than an end goal of data exploration in unsupervised learning.