HOTSPOT - (Topic 2)
To complete the sentence, select the appropriate option in the answer area.
Solution:
In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Does this meet the goal?
Correct Answer:
A
- (Topic 5)
Your company manufactures widgets.
You have 1.000 digital photos of the widgets.
You need to identify the location of the widgets within the photos. What should you use?
Correct Answer:
B
- (Topic 5)
You have an Internet of Things (loT) device that monitors engine temperature.
The device generates an alert if the engine temperature deviates from expected norms.
Which type of Al workload does the device represent?
Correct Answer:
C
HOTSPOT - (Topic 5)
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Solution:
Does this meet the goal?
Correct Answer:
A
DRAG DROP - (Topic 3)
Match the types of machine learning to the appropriate scenarios.
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: Each correct selection is worth one point.
Solution:
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.
Does this meet the goal?
Correct Answer:
A
