Answer / Khushboo Srivastava

A typical machine learning project involves several steps: problem understanding, data collection and preprocessing, feature engineering, model selection and training, evaluation, tuning, and deployment. These steps may be iterated multiple times to improve performance.

How do bias and variance play out in machine learning?

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What is bias-variance trade-off in machine learning?

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What are the different categories you can categorize the sequence learning process?

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Explain how do classification and regression differ?

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Tell us what is the difference between supervised and unsupervised machine learning?

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What is data normalization in ml?

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

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What is the activation function in Machine Learning?

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What is the convex function in machine learning?

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What do you understand by algorithm independent machine learning?

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What are the two paradigms of ensemble methods?

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Is machine learning a good career?

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