ראיון עבודה machine learning

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You are using K-nearest neighbor and you have a K of 1. What are you likely to see when you train the model?

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שאלה #69267

done		high variance and low bias
		low bias and low variance
		low variance and high bias
		high bias and high variance

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Are data model bias and variance a challenge with unsupervised learning?

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שאלה #69268

		No, data model bias and variance are only a challenge with reinforcement learning.
done		Yes, data model bias is a challenge when the machine creates clusters.
		Yes, data model variance trains the unsupervised machine learning algorithm.
		No, data model bias and variance involve supervised learning.

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Which choice is best for binary classification?

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שאלה #69269

		K-means
done		Logistic regression
		Linear regression
		Principal Component Analysis (PCA)

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With traditional programming, the programmer typically inputs commands. With machine learning, the programmer inputs

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שאלה #69270

		supervised learning
sentiment_very_satisfied		data
		unsupervised learning
		algorithms

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Why is it important for machine learning algorithms to have access to high-quality data?

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שאלה #69271

		It will take too long for programmers to scrub poor data.
		If the data is high quality, the algorithms will be easier to develop.
		Low-quality data requires much more processing power than high-quality data.
done		If the data is low quality, you will get inaccurate results.

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In K-nearest neighbor, the closer you are to neighbor, the more likely you are to

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שאלה #69272

done		share common characteristics
		be part of the root node
		have a Euclidean connection
		be part of the same cluster

נערך לפני שנתיים stav shlomovich

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In the HBO show Silicon Valley, one of the characters creates a mobile application called Not Hot Dog. It works by having the user take a photograph of food with their mobile device. Then the app says whether the food is a hot dog. To create the app, the software developer uploaded hundreds of thousands of pictures of hot dogs. How would you describe this type of machine learning?

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שאלה #69273

		Reinforcement machine learning
		unsupervised machine learning
done		supervised machine learning
		semi-supervised machine learning

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You work for a large pharmaceutical company whose data science team wants to use unsupervised learning machine algorithms to help discover new drugs. What is an advantage to this approach?

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שאלה #69274

		You will be able to prioritize different classes of drugs, such as antibiotics.
		You can create a training set of drugs you would like to discover.
done		The algorithms will cluster together drugs that have similar traits.
		Human experts can create classes of drugs to help guide discovery.

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In 2015, Google created a machine learning system that could beat a human in the game of Go. This extremely complex game is thought to have more gameplay possibilities than there are atoms of the universe. The first version of the system won by observing hundreds of thousands of hours of human gameplay; the second version learned how to play by getting rewards while playing against itself. How would you describe this transition to different machine learning approaches?

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שאלה #69275

done		The system went from supervised learning to reinforcement learning.
		The system evolved from supervised learning to unsupervised learning.
		The system evolved from unsupervised learning to supervised learning.
		The system evolved from reinforcement learning to unsupervised learning.

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The security company you work for is thinking about adding machine learning algorithms to their computer network threat detection appliance. What is one advantage of using machine learning?

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שאלה #69276

done		It could better protect against undiscovered threats.
		It would very likely lower the hardware requirements.
		It would substantially shorten your development time.
		It would increase the speed of the appliance.

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