Answer / Arjun Tyagi

Machine learning (ML) is a subset of Artificial Intelligence (AI), which refers to a system's ability to perform tasks that would normally require human-like intelligence. ML involves training algorithms to learn patterns in data, make decisions, and improve through experience without being explicitly programmed. AI encompasses broader concepts like knowledge representation, reasoning, natural language processing, and robotics.

How is a decision tree pruned?

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What's the f1 score? How would you use it?

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What is 'naive' in the Naive Bayes classifier?

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Explain the Algorithm Technique of Unsupervised Learning in Machine Learning?

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Why are vectors and norms used in machine learning?

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What’s the trade-off between bias and variance?

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

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What is Bayes theorem in artificial intelligence?

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Is bayesian a machine learning?

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Which one would you prefer to choose – model accuracy or model performance?

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Can you explain what is the difference between inductive machine learning and deductive machine learning?

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How can we use your machine learning skills to generate revenue?

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