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Top ML (Machine Learning) Interview Questions and Answers

28/Nov/2020 | 10 minutes to read

Here is a List of essential Machine Learning Interview Questions and Answers for Freshers and mid level of Experienced Professionals. All answers for these Machine Learning questions are explained in a simple and easiest way. These questions will help you to clear your next Job interview.

Machine Learning Interview Questions

Machine Learning Interview Questions and Answers

These questions are targeted for a Machine Learning Engineer. You must know the answers of these frequently asked Machine Learning questions to clear the AI/ML interview.

1. What is Machine Learning?

2. How is Machine Learning related to Artificial Intelligence?

3. What is the Supervised and Unsupervised learning?

Machine Learning provides mainly two types of techniques, one is Supervised Learning and second is Unsupervised Learning.

  • Supervised Learning - It is the process of training the machines from a data set which has already correct answers, so that when machine performs the task on any new data variables then it should predict correct results based on the analysis of trained data. In Supervised learning there is a input data, output data and an algorithm to map the function from input to correct output. Function is mapped so approximately that when new data input comes then correct output can be predicted.
    For example, if you show 2 pictures one of orange and one mango to a baby and tell him like first one is Orange and second one is Mango, then baby has learned from pictures if you show him 3rd picture of Orange then he can tell like it's orange because he has already learned from previous labeled data.
  • Unsupervised Learning - It is the process of finding interesting patterns, hidden structures from data. So There is no input variables and algorithms in Unsupervised learning. So In Unsupervised Learning Data is not labeled like in Supervised learning.
    For example, if you have shown 6 pictures of dogs(3) and cats(3) to a baby who is not able to identify the dogs and cats then he can only tell there are 3 pictures of type one and other 3 are of type two. But he can not tell that there are 3 dogs and 3 cats because there is no labeling of data.

4. How will you decide that Which machine learning classifier you should choose?

5. What is the Learning Curve in Machine Learning? Explain it.

6. What do understand by inductive bias in machine learning?

7. Differentiate deep learning versus machine learning.

8. What are the commonly used programming languages in Machine Learning?

9. What is Machine Learning Data Modeling?

10. List some applications of Machine Learning?

11. What's the Naive Bayes classifier in Machine Learning?

12. What is the use of meshgrid in Python / NumPy?

13. Differentiate classification vs clustering in data mining?

14. How will you interpret "loss" and "accuracy" for a machine learning model

15. What's the difference between Loss, accuracy, validation loss and Validation accuracy in Machine Learning?

16. How will you divide a dataset into training and validation sets?

17. What's the usage of F1 score in Machine Learning?

18. What is an imbalanced dataset? How will you handle it?

19. How will you choose from classification and regression??

20. What is KNN? How it's different from k-means clustering?

21. Differentiate L1 and L2 regularization.

22. Differentiate Type I and Type II error.

23. Differentiate generative and discriminative model.

24. What is AUC curve? What lies on x-axis and y-axis of AUC curve in Machine learning?

25. What is ROC curve? How it is used?

26. What is Imbalanced class problem in Machine Learning?

27. What is logistic regression in Machine learning?

28. What is random forest in machine learning?

29. Give some examples of Supervised and Unsupervised Machine Learning Algorithms.

30. What is gradient descent algorithm in Machine Learning?

31. What is Tensorflow in Machine learning?

32. Explain BERT.

33. Explain CNN and RNN.

34. Explain transformers in Machine Learning.

35. What is F1 score in machine learning?

36. What is Text summarization?

Some General Interview Questions for Machine Learning

1. How much will you rate your self in Machine Learning?

When you attend an interview, Interviewer may ask you to rate your self in specific Technology like Machine Learning, So It's depend on your knowledge and work experience in Machine Learning.

2. What challenges did you face while working on Machine Learning?

This question may be specific to your technology and completely depends on your past work experience. So you need to just explain the challenges you faced related to Machine Learning in your Project.

3. What was your role in last Project related to Machine Learning?

It's based on your role and responsibilities assigned to you and what functionality you implemented using Machine Learning in your project. This question is generally asked in every interview.

4. How much experience do you have in Machine Learning?

Here you can tell about your overall work experience on Machine Learning.

5. Have you done any Machine Learning Certification or Training?

It's depend on candidate like you have done any Machine Learning training or certification. Certifications or trainings are not essential but good to have.


We have covered some frequently asked Machine Learning Interview Questions and Answers to help you for your Interview. All these Essential Machine Learning Interview Questions are targeted for mid level of experienced Professionals and freshers.
While attending any Machine Learning Interview if you face any difficulty to answer any question please write to us at Our IT Expert team will find the best answer and will update on portal. In case if we find any new Machine Learning questions, we will update the same here.

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