Course Content Quiz
Quiz
•
Computers, Other
•
Professional Development
•
Practice Problem
•
Hard
mohesh chandran
Used 3+ times
FREE Resource
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30 questions
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1.
MULTIPLE SELECT QUESTION
45 sec • 1 pt
1. What is Machine Learning? (Choose 3 Answers)
Artificial Intelligence
Machine Learning
Data Statistics
Deep Learning
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Deep Learning is a subfield of Machine Learning
TRUE
FALSE
3.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
Artificial Intelligence is superset of ________________ & _____________________
Machine Learning & Neural Networks
Machine Learning & Deep Learning
Deep Learning & Neural Networks
4.
MULTIPLE SELECT QUESTION
2 mins • 1 pt
Which of the following statements are incorrect. (choose 3 option)
Reinforcement Learning is also an unsupervised learning
Having human in the loop is not necessary especially when the data are getting bigger because it will cost more and AI helps to be more cost-effective
Supervised learning is a complex method compared to unsupervised learning
Regression has continuous output variable that makes it a part of unsupervised learning
All of the above
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of the statements are true
AI can recognize handwriting really well
AI can recognize words spoken in accents
AI can visually describe an image
AI can perform multiple robotic tasks at once
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
The steps: definition, value, stakeholders, priority & investment corresponds to which of the following stages of AI approach
Data
Business Case
Deploy & Measure
Active Learning & Tuning
7.
MULTIPLE CHOICE QUESTION
2 mins • 1 pt
What is the correct steps would you work to gauge the effectiveness of a machine learning model?
I – You make decision and apply necessary changes from the obtained metrics.
II – You use measures such as the F1 score, the accuracy, and the confusion matrix.
III – You implement a choice selection of performance metrics.
IV – You would split the dataset into training and test sets, or perhaps use cross-validation techniques to further segment the dataset into composite sets of training and test sets within the data.
I, II, III, IV
IV, III, II, I
I, III, II, IV
IV, II, III, I
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