Answer / Lajwant Singh

Similarities: Both Machine Learning and Human Learning involve acquiring knowledge from data, making predictions based on learned patterns, and improving performance over time. Differences: Human Learning is influenced by a variety of factors such as emotions, experience, and culture, whereas Machine Learning relies solely on the data provided to it.

Which is best language for machine learning?

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Tell us what is your training in machine learning and what types of hands-on experience do you have?

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What is bagging and boosting in Machine Learning?

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What is the decision tree classification?

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What is your favorite use case for machine learning models?

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Explain the difference between bias and variance?

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What is naive bayes in machine learning?

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What type of learning is needed when the system needs to adapt to rapidly changing data?

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How difficult is machine learning?

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What according to you, is more important between model accuracy and model performance?

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What is pruning in decision trees and how is it done?

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Why instance based learning algorithm sometimes referred as Lazy learning?

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