AI Machine Learning Interview Questions, Answers for Freshers and Experienced asked in various Company Job Interviews

AI Machine Learning Interview Questions

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Explain what is the difference between inductive machine learning and deductive machine learning?

Which do you think is more important: model accuracy or model performance?

What cross-validation technique would you use on a time series dataset?

What do you think of our current data process?

When should you use classification over regression?

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

Explain machine learning in to a layperson?

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

What are your favorite use cases of machine learning models?

An example where ensemble techniques might be useful?

What is the difference between a generative and discriminative model?

How a roc curve works?

What is bayes' theorem? How is it useful in a machine learning context?

How would you handle an imbalanced dataset?

How do you handle missing or corrupted data in a dataset?

Un-Answered Questions { AI Machine Learning }

Why do we need a validation set and test set? What is the difference between them?

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

What do you know about bayesian networks?

What is the objective of machine learning?

What are confounding variables?

Which language is best for deep learning?

How will you design an email spam filter?

What kind of problems lend themselves to machine learning?

What is the difference between bayesian and frequentist?

What is bayes' theorem? How is it useful in a machine learning context?

Which os is best for machine learning?

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Differentiate between data science, machine learning and ai.

Explain how do you ensure you're not overfitting with a model?

Which language is better for machine learning r or python?

Explain why is naive bayes better than decision tree?

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