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Top Machine Learning Interview Questions and Answers

30/Sep/2021 | 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 basic, advanced and latest Machine Learning questions will help you to clear your next Job interview.

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 interview questions to clear Data Scientist or Machine Learning Engineer interviews. This list contains the questions from classic ML to deep learning, supervised and unsupervised learning. All questions and answers are prepared by experienced Data Scientists and Machine Learning Engineers.

1. What is Machine Learning?

Machine Learning is a data science technique used by Data Scientists to build, train and manage models based on existing data. These models can run on large volumes of data to forecast future behaviors, trends and fraud detection without being explicitly programmed.
When you do online shopping, machine learning helps to show recommended products based on your shopping you have made. Machine Learning helps to detect frauds by comparing your transaction with a database of transactions whenever you swipe your card. Machine learning can be of any type from classic machine learning to deep learning, supervised and unsupervised learning.

2. How does Machine Learning relate to AI (Artificial Intelligence)?

Machine Learning is the sub field or part of AI - Artificial Intelligence. It trains the machines or systems to learn from experience or by the use of data without any additional programming. AI is at a broader level that has different subfields such as Deep Learning, Computer vision Neural Network NLP etc. For more visit Parts of AI.

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 a 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 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?

A classifier is an algorithm used by machines to classify the data. Selection of algorithm in machine learning depends on many factors including:

  • Categorize the Problem - Categorize the problem based on input and output.
  • Understand your Data - Determine the quality, size and nature of the data you have.
  • Find Available Algorithms - Identify the algorithms which are available to implement in a defined timeline.
  • Implement the Algorithm - Compare different algorithms performance and analyze with different datasets.
  • Hyperparameter optimization - Choose the set of optimal hyperparameters to control the learning process of an algorithm.
  • Urgency of the task
  • What you want to achieve from data
For more visit Choose right Machine Learning Algorithm and Which machine learning algorithm should I use?

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

6. What do you understand about inductive bias or learning 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 a 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 is it different from k-means clustering?

21. Differentiate L1 and L2 regularization.

22. Differentiate Type I and Type II error.

23. Differentiate generative and discriminative models.

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

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

26. What is an Imbalanced class problem in Machine Learning?

27. What is logistic regression in Machine learning?

28. What is the random forest algorithm in machine learning?

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

30. What is a 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 an F1 score in machine learning?

36. What is Text summarization?

Some General Interview Questions for Machine Learning

1. How much will you rate yourself in Machine Learning?

When you attend an interview, Interviewer may ask you to rate yourself in a 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 the 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 depends on the candidate whether you have done any Machine Learning training or certification. Certifications or training 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 the portal. In case we find any new Machine Learning questions, we will update the same here.