Answer / Hari Prakash

Machine learning is a broader field that includes various types of models, such as supervised learning, unsupervised learning, and reinforcement learning. Regression is a specific type of supervised learning model used for predicting continuous outcomes.

Can you explain how do you handle missing or corrupted data in a dataset?

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When does regularization become necessary in machine learning?

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Which operating system is best for machine learning?

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Which language is better for machine learning?

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What is the baseline in machine learning?

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Do you have research experience in machine learning?

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How do we separate one dimensional, two dimensional and three-dimensional data?

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How python can be used in machine learning?

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How is F1 score is used?

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When should one use mean absolute error over root mean square error as a performance measure for regression problems?

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Is it better to have too many false positives or too many false negatives? Explain.

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Tell me how a roc curve works?

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