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

AI Machine Learning Interview Questions

Questions Answers Views Company eMail

Give a drawback of gradient descent ?

Logistic regression gives probabilities as a result then how do we use it to predict a binary outcome?

What is adagrad algorithm in machine learning?

106

What kind of problems lend themselves to machine learning?

What is the difference between supervised and unsupervised learning?

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

184

What are standardization and normalisation?

107

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

Does 100% precision mean that our model predicts all the values correctly?

Why do we convert categorical variables into factor? Which function is used in r to perform the same?

117

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

Tell me what is a recommendation system?

180

Tell us how do classification and regression differ?

Tell us what evaluation approaches would you work to gauge the effectiveness of a machine learning model?

Explain me what cross-validation technique would you use on a time series dataset?

131

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Un-Answered Questions { AI Machine Learning }

What is accuracy machine learning?

108

Why is naive bayes better than decision tree?

138

What are the two classification methods that SVM ( Support Vector Machine) can handle?

What is machine learning good for?

Is Python enough for machine learning?

100

What is PAC Learning?

109

Why do we convert categorical variables into factor?

104

What is feature engineering?

What are the five popular algorithms we use in machine learning?

175

Tell us how do you handle missing or corrupted data in a dataset?

Why is naïve bayes machine learning algorithm naïve?

152

Explain the difference between type I and type ii error?

How would you approach the “Netflix Prize” competition?

174

Explain false negative, false positive, true negative and true positive with a simple example?

Explain the k-nearest algorithm different from the knn clustering?

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