Answer / Narad Kumar Bhardwaj

My favorite use case for machine learning models is image recognition and classification, particularly in the healthcare sector, such as identifying abnormalities in medical images.

What is the “kernel trick” and how is it useful?

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List out some important methods of reducing dimensionality.

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Why does overfitting happen?

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What is the meaning of overfitting in machine learning?

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What evaluation approaches would you work to gauge the effectiveness of a machine learning model?

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How will you explain machine learning into a layperson?

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What was bayes’ theorem? How was it useful in a machine learning context?

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Why do ensembles typically have higher scores than individual models?

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Which os is good for machine learning?

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How does machine learning differ from deep learning?

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Explain me how would you handle an imbalanced dataset?

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What do you understand by underfitting?

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