Machine Learning Fundamental Concepts Exercises (Part 3)

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Question 1 of 8

1. Question
What is regression?
- Learning problem area that is seeking to find a pattern or trend that fits a line
- Learning problem area where the predicted outcome is the class (discrete) to which the data belongs.
- Learning problem area that is seeking to automatically identify groupings and related data
- None of the above
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Question 2 of 8

2. Question
What is clustering?
- Learning problem area that is seeking to find a pattern or trend that fits a line
- Learning problem area where the predicted outcome is the class (discrete) to which the data belongs.
- Learning problem area that is seeking to automatically identify groupings and related data
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Question 3 of 8

3. Question
Match these Unsupervised learning ideas to their definitions.
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- Clustering
- Anomaly Detection
- Association
- Auto-encoding
- Identify data into related groups among multiple dimensions
- Which data is not like the pattern
- What data is related to other data
- Find ways to summarize data so that it can be re-created
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Question 4 of 8

4. Question
What is a variable or feature that changes for each data point that the machine learning system needs to learn?
- Learning Curve
- Dimension
- Algorithm
- Inference
- Training data
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Question 5 of 8

5. Question
What is The Curse of Dimensionality?
- The more dimensions (factors, variables) you need to learn and classify, the less that the samples of data you have will be sparse (few examples for each category). This means you use small amounts of data to make sure there are enough real examples to train the systems for predictable results.
- The less dimensions (factors, variables) you need to learn and classify, the more that the samples of data you have will be sparse (few examples for each category). This means you need very little data to make sure there are enough real examples to train the systems for predictable results
- The more dimensions (factors, variables) you need to learn and classify, the more that the samples of data you have will be sparse (few examples for each category). This means you need lots and lots of data to make sure there are enough real examples to train the systems for predictable results.
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Question 6 of 8

6. Question
Dimensionality reduction is:
- The process of moving from three dimensions to four dimensions
- The process of increasing the amount of data needed for training
- The process of reducing the number of random variables under consideration
- The process of reducing the number of data points needed for training
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Question 7 of 8

7. Question
Some believe that you can use reinforcement learning for all learning needs
- True
- False
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Machine Learning Fundamental Concepts Exercises (Part 3)

Machine Learning Fundamental Concepts Exercises (Part 3)

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