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What is machine learning?
What is machine learning?
Machine learning is a branch of artificial intelligence and computer science that focuses on the use of data and algorithms to imitate the way humans learn, gradually improving accuracy.
What is a feature in the context of machine learning?
What is a feature in the context of machine learning?
A feature is a measurable property or parameter of the data-set.
What is the role of a machine learning model?
What is the role of a machine learning model?
A machine learning model is the mathematical representation of a real-world process, also known as 'hypothesis'.
How does the accuracy of predicted output in machine learning depend on the amount of data?
How does the accuracy of predicted output in machine learning depend on the amount of data?
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What is the main purpose of using unsupervised learning?
What is the main purpose of using unsupervised learning?
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How does unsupervised learning resemble human learning?
How does unsupervised learning resemble human learning?
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Why is unsupervised learning important in real-world scenarios?
Why is unsupervised learning important in real-world scenarios?
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What is the primary method used in unsupervised learning to group objects with similarities?
What is the primary method used in unsupervised learning to group objects with similarities?
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What is positive reinforcement learning?
What is positive reinforcement learning?
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What is a potential consequence of too much positive reinforcement?
What is a potential consequence of too much positive reinforcement?
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How does negative reinforcement learning differ from positive reinforcement learning?
How does negative reinforcement learning differ from positive reinforcement learning?
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What are the three traits that define a well-posed learning problem?
What are the three traits that define a well-posed learning problem?
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What is the incident called when a machine learning model continues to show the same recommendations to the customer despite the change in customer requirements?
What is the incident called when a machine learning model continues to show the same recommendations to the customer despite the change in customer requirements?
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What is the term for the situation when certain elements of the dataset are heavily weighted or given more importance than others, leading to inaccurate results?
What is the term for the situation when certain elements of the dataset are heavily weighted or given more importance than others, leading to inaccurate results?
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What type of data consists of numerical values and anything that is measured by numbers?
What type of data consists of numerical values and anything that is measured by numbers?
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How can the challenge of bad recommendations and data drift in machine learning models be overcome?
How can the challenge of bad recommendations and data drift in machine learning models be overcome?
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