Machine Learning Interview Questions and Tips for Answering Them

Written by Coursera • Updated on

A machine learning interview guide, with examples of interview questions and how you might approach them. You’ll learn what to do in unique interview situations and how to prepare so you are confident in the interview process.

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Technical and programming interview questions are common for machine learning roles. Interviewers are looking for responses that demonstrate knowledge of methods and basic concepts.

Experience and certifications in machine learning (ML) can open doors to many jobs, including machine learning engineer, data scientist, cybersecurity analyst, cloud architect, and more. If you're preparing for an interview relating to machine learning, there are some common questions that you may encounter.

To help you prepare and build confidence, go over these common questions, answer tips, and interview prep advice. 

What to expect in a machine learning interview

Depending on the role, the questions and tasks may vary. There will, however, always be a round of live questions to assess your knowledge of machine learning techniques and your ability to perform under pressure. Here are four example questions, with suggestions on how to answer.

1. How do you handle missing or corrupted data in a data set?

This question helps demonstrate your problem-solving skills and experience dealing with corrupted data.  

How to answer: A great way to answer this question is to suggest methods that may solve the problem. It’s a good idea to include examples and more than one solution to help show your understanding of datasets.  

2. Explain the difference between deep learning, artificial intelligence (AI), and machine learning.

This question tests your knowledge in the field. The interviewer may want to know that you can explain the subtle differences between each technology. 

How to answer: Make it clear that you understand that machine learning is a subset of AI and that deep learning is a subset of machine learning by describing each.

Read more: Deep Learning vs. Machine Learning

3.  Describe your favorite machine learning algorithm.

This question is an opportunity for you to show your preferences and individual skills while also showing that you have a deep understanding of various ML algorithms. 

How to answer: Give reasons for your choice and make some direct comparisons to other algorithms, so it’s clear your expertise extends further than the ML algorithm you are highlighting. Use examples from your career and studies to provide evidence of what you’re saying.

4. What's the difference between unsupervised learning and supervised learning?

This is another common question aimed at assessing your understanding of certain techniques.

How to answer: Make it clear that you know the distinction between labeled and unlabeled data sets. If you have your work samples, it’ll help show your understanding.

Tips to help ace your machine learning interview

Aside from practicing the questions above, here are some additional tips to help make a great impression and show your suitability for the role.

Apply concepts and work on your relevant skills.

It’s important that you have real-life examples to pair with your answers. Recruiters are usually looking for experience as well as knowledge, and the more experience you can demonstrate while applying the concepts, the better you’ll do. 

It’s also beneficial to show that you’re always learning and developing your skills. Show how driven you are to improve yourself and your expertise during the interview process. A recruiter may be impressed to see that you’re always striving to improve and grow. 

Focus on what you know.

If you mention a method in your answer, chances are the interviewer will ask you more about it. Set yourself up for success with relevant answers that focus on your areas of expertise and experience.

Research the company. 

Researching the company will allow you to tailor your responses and examples according to the business. It can also help you learn more about the company's values and work culture and share how you align with them in the interview.

Be sure to write clearly.

Part of the interview might include specific tests or written tasks. If this is the case, make sure you write clearly. You may be tested on your scientific rigor, so include charts and citations and introduce notions when necessary. 

If you’re unsure of an answer, it’s OK to say so.

It’s possible that you'll get a question you don’t know the answer to. A straightforward way to approach this is to say, “I’m not sure of the answer, but here is how I would go about finding out ... .”

Take your time to answer. In this situation, it may help to work through your answer out loud. Talking through your thought processes may allow the interviewer to prompt you with additional questions. Remember, they want to help you get to an answer and understand your problem-solving skills.

Next steps

Brush up on your machine learning expertise with a self-paced course from an industry leader, like Machine Learning Rock Star from SAS or Machine Learning from Stanford. Upon completing each course or Specialization, you’ll have a certificate to add to your resume or LinkedIn profile.  

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Written by Coursera • Updated on

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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