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 }

Tell us where do you usually source datasets?

How much data will you allocate for your training, validation and test sets?

What are the three stages to build any model in machine learning?

What is regression in machine learning with example?

Explain true positive, true negative, false positive, and false negative in confusion matrix with an example.

What is the difference between inductive and deductive learning?

What is a model selection in machine learning?

Why do we need to convert categorical variables into factor?

Explain me a hash table?

What is stratified cross-validation and when should we use it?

What is bias-variance decomposition of classification error in ensemble method?

What do you mean by parametric models?

Why Accuracy is important in machine learning?

Explain the difference between bayes and naive bayes?

Explain the Algorithm of Neural Networks (back propagation) in Machine Learning?

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