A team must build a model that determines whether people in images are wearing the company’s retail brand. Given a labeled image dataset, which type of machine learning model is most appropriate?

A Convolutional Neural Network (CNN) is the most appropriate model for this task because it excels at image classification and object detection. CNNs are specifically designed to process pixel data, identify patterns, and learn hierarchical features within images, making them ideal for determining if a specific brand is present. Latent Dirichlet Allocation (LDA) is used for topic modeling in text, not image analysis. Recurrent Neural Networks (RNNs) are best suited for sequential data like time series or natural language processing, not static images. K-means clustering is an unsupervised learning algorithm used for grouping similar data points, not for classifying specific objects within images based on labeled data.