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

What is the meaning of overfitting in machine learning?

What do you understand by machine learning?

111

Why overfitting occurs?

109

Differentiate between inductive learning and deductive learning?

103

When does regularization become necessary in machine learning?

202

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

173

What are the functions of unsupervised learning?

What do you understand by decision tree in machine learning?

What is regularization? What kind of problems does regularization solve?

What do you understand by underfitting?

Why do we need to convert categorical variables into factor? Which functions are used to perform the conversion?

What are the similarities and differences between bagging and boosting in machine learning?

What do you understand by the f1 score?

What do you understand by cluster sampling?

What are the necessary steps involved in machine learning project?

177

Un-Answered Questions { AI Machine Learning }

What is the difference between machine learning vs data mining?

163

What are the 3 types of ai?

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

What evaluation approaches would you work to gauge the effectiveness of a machine learning model?

What is classification and regression in machine learning?

199

What is symbolic learning in AI?

What is calibration in machine learning?

100

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

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

What is Model Selection in Machine Learning?

123

Explain the function of Supervised Learning?

What is the f1 score? How would you use it?

149

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

What is machine learning artificial intelligence?

What is pruning in decision trees and how is it done?

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