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?

100

What do you understand by machine learning?

115

Why overfitting occurs?

111

Differentiate between inductive learning and deductive learning?

105

When does regularization become necessary in machine learning?

208

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

175

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?

179

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Un-Answered Questions { AI Machine Learning }

Differentiate supervised and unsupervised machine learning.

232

What are the different types of classifiers?

105

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

100

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

What is the most frequent metric to assess model accuracy for classification problems?

144

Explain the two components of Bayesian logic program?

What is Seq2Seq Tensorflow?

121

What measure how far the predictions are from the actual values in machine learning?

What are some methods of reducing dimensionality?

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

What is the trade-off between bias and variance?

Is coding required for machine learning?

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

What are the best public data sets for machine learning?

You are given a dataset where the number of variables (p) is greater than the number of observations (n) (p>n). Which is the best technique to use and why ?

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