Machine Learning Interview Questions (+ Tips to Answer Them)

Written by Coursera Staff • Updated on

Machine learning interviews allow you to showcase your skills, knowledge, and work. Read on to find some of the most common questions you can expect to be asked and find tips on answering them confidently.

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Technical and programming interview questions are common for machine learning roles. Recruiters use interviews to assess a qualified candidate's knowledge of fundamental machine-learning methods and concepts.

This is your chance to stand out from the crowded applicant pool and highlight the qualities that make you a great candidate for the job. Experience and certifications in machine learning (ML) can open doors to many jobs, such as machine learning engineer, data scientist, cybersecurity analyst, cloud architect, and more. But to get these roles, you'll need to demonstrate to recruiters that you know your stuff.

If you're preparing for an interview with a machine learning focus, then there are some common interview questions that you should prepare for. To help you get started and build the confidence you need to ace your following interview, here are some of the most common machine learning interview questions you'll encounter and some interview prep advice and tips you should keep in mind. 

What to expect in a machine learning interview

Machine learning interviews allow you to highlight your skills, knowledge, and work beyond what you can put on your resume.

Depending on the role, the interview questions and tasks you're expected to perform may vary. Typically, you can expect a round of live questions to assess your knowledge of machine-learning techniques and your ability to perform under pressure.

Here are four of the most common interview questions, along with tips on how to answer each one:

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. At the most basic level, this question asks about your process to see how you work.

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 data sets.  

At the same time, ensure you emphasise the concrete steps you take to solve these problems, so your interviewer can get a clearer picture of what you look like when you're in your element.

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 concept to ensure a strong grasp of foundational machine learning knowledge. 

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. Rather than just stating the obvious, use examples in your response to show that you have entirely mastered these important concepts.

3.  Describe your favourite machine learning algorithm.

This question allows you to show your preferences and individual skills while showing a deep understanding of various ML algorithms. Whether you like the simplicity of a common classification algorithm or a more complex one that acts as the basis for a predictive model, this is your chance to show your passion for your field. 

Some common machine learning algorithms you might consider mentioning include:

  • Linear regression

  • Logistic regression

  • Naive Bayes

  • Decision trees

  • Random forest algorithm

  • K-nearest neighbour (KNN)

  • K means

How to answer: The exact algorithm you mention is only as important as your reasons for selecting it. You can use this question as an opportunity to showcase your knowledge of the field by drawing direct comparisons to other algorithms. Hence, it’s clear your expertise extends further than the ML algorithm you are highlighting.

As you answer the question, use examples from your career and studies to support your answer. Focusing on concrete examples will also allow you to highlight the work you've already done that can prepare you for the job.

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

This is another common question to assess your understanding of foundational machine learning techniques, which will likely undergird much of your future work.

How to answer: Make it clear that you know the distinction between labelled and unlabelled training data sets and how they're used to create different machine learning models, such as classification or linear regression. Also, highlight any machine learning projects you have undertaken and explain how you used supervised or unsupervised learning to accomplish them.

Tips to help ace your machine learning interview

The best way to ace an interview is to prepare in advance. Aside from practicing the above interview questions, here are some additional tips to help make a great impression and show your suitability for the role:

1. Apply concepts and work on your relevant skills.

Throughout your interview, connect your answers with real-life examples, especially ones that reference your work. Recruiters are usually looking for experience and knowledge, and the more experience you can demonstrate while discussing machine learning concepts, the more you'll be able to highlight your preparedness for the job. 

It’s also beneficial to show that you continuously learn and develop your skills. During the interview, show how driven you are to improve yourself and your expertise. A recruiter may be impressed that you always strive to improve and grow. 

2. Focus on what you know.

If you mention a method in your answer, chances are the interviewer will ask you more about it. You can set yourself up for success with relevant answers on your expertise and experience.

3. Research the company. 

Researching the company will allow you to tailor your responses and examples 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.

One way to get an insider's view of the company or industry is to conduct an informal informational interview or read employee reviews online.

4. Be sure to write clearly.

Part of the interview might include specific tests or written tasks. If this is the case, write clearly and thoroughly explain your answers. You may be tested on your scientific rigour, so include charts and citations and introduce notions when necessary.  For example, whether you're tasked with creating a simple or complex model, showing every step of the process is essential so your potential employer can see your work.

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

You may be asked a question you don’t know the answer to. A straightforward approach is to say, ‘I’m not sure of the answer, but here is how I would find out ...’

Take your time answering. In this situation, work through your answer out loud. Discussing your thought processes may allow the interviewer to ask more questions. Remember, they want to help you reach 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 SAS' Machine Learning Rock Star, or hone your skills through Andrew Ng's three-course Machine Learning Specialisation offered by Stanford and DeepLearning.AI. Upon completing each course or Specialisation, you’ll also have a certificate to add to your resume, portfolio, or LinkedIn profile—a potential indicator of your skills and job preparedness.  

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