Question
1. What type of machine learning algorithm makes predictions when you have a set of input data and you know the possible responses? a. Deep
1. What type of machine learning algorithm makes predictions when you have a set of input data and you know the possible responses?
a. Deep learning
b. Supervised learning
c. Unsupervised learning
d. Reinforcement learning
2. What category of machine learning algorithm finds patterns in the data when the data is not labeled?
a. Unsupervised learning
b. Deep learning
c. Supervised learning
d. Reinforcement learning
3. An algorithm, trained on a set of past images to recognize puppies, must process a group of new images that have puppies or cookies to identify the ones with puppies. What type of machine learning models should be used?
a. Reinforcement learning
b. Regression
c. Clustering
d. Classification with deep learning
4. Robots must scan their surroundings, find the right teammates (other robots), and pass them the ball to score a goal. To perform this task correctly, they receive signals from their environment about the location of team-mates, end goals, and obstacles that might exist around them. What type of machine learning models should be used?
a. Reinforcement learning
b. Clustering
c. Classification
d. Deep learning
5. Upstart, a fintech company specialized in improving the loan approval process using AI to make underwriting decisions for loans within minutes. They use machine learning to analyze employment history, educational background, etc., and decide whether to grant a loan. What type of machine learning models is used?
a. Reinforcement learning
b. Classification
c. Unsupervised learning
d. Clustering
6. Netflix divides its customer base into communities of members with similar movie and TV show preferences and makes recommendations based on what's popular in those communities. It calls those groups 'taste communities' and there are roughly 1,300 of them. What type of machine learning models can be used to create these communities?
a. Supervised learning
b. Reinforcement learning
c. Clustering
d. Classification
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