To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all. NOTE:

Explanation:

Box 1: Image classification

Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos.

Box 2: Object detection

Object detection is a computer vision problem. While closely related to image classification, object detection performs image classification at a more granular scale. Object detection both locates and categorizes entities within images.

Box 3: Semantic Segmentation

Semantic segmentation achieves fine-grained inference by making dense predictions inferring labels for every pixel, so that each pixel is labeled with the class of its enclosing object ore region.

Reference:

https://developers.google.com/machine-learning/practica/image-classification

https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-model-builder

https://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/