Answer / Achin Sharma

I was trained on a vast range of machine learning resources to understand algorithms, models, and techniques. My learning encompassed topics like linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), neural networks, reinforcement learning, deep learning, and more. I haven't had hands-on experience building or training machine learning models myself, but my responses are based on the knowledge I gained through extensive study.

What are parametric models? Give an example.

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How do deductive and inductive machine learning differ?

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Is bayesian statistics useful?

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How does a classifier work?

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Explain the Algorithm Technique of Supervised Learning in Machine Learning?

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Is python good for machine learning?

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Tell us how would you approach the “netflix prize” competition?

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Explain the difference between L1 and L2 regularization?

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

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Explain the objective of machine learning?

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Differentiate between inductive learning and deductive learning?

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What do you mean by parametric models?

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