Machine Learning Interview Questions (+ Tips to Answer Them)

Written by Coursera • Updated on

Machine learning interviews give you the opportunity 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 how you can answer them with confidence.

[Featured Image]  A group of people are having a meeting at a table.

Technical and programming interview questions are common for machine learning roles. Rather than trying to catch you out with left-field questions, though, 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 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 next interview, here are some of the most common machine learning interview questions you'll encounter and alongside some interview prep advice and tips you should keep in mind. 

What to expect in a machine learning interview

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

Depending on the role, the interview questions you're asked and tasks you're expected to perform may vary. Typically, though, 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 is asking you 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 datasets.  

At the same time, make sure that you emphasize the concrete steps you take to solve these kinds of 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 that you have 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 total mastery of these important concepts.

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. Whether you're in love with 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 neighbor (KNN)

  • K means

How to answer: The exact algorithm you mention isn't as important as the reasons you give for selecting it. Use this question as an opportunity for you to showcase your knowledge of the field by drawing direct comparisons to other algorithms, so it’s clear your expertise extends further than the ML algorithm you are highlighting.

As you are answering the question, make sure to 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.

Read more: 7 Machine Learning Algorithms to Know

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

This is another common question aimed at assessing 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 labeled and unlabeled training datasets, and how they're used to create different types of machine learning models, such as classification or linear regression models. You might also consider highlighting any machine learning projects you have undertaken and explaining how you used either supervised or unsupervised learning to accomplish them.

video-placeholder
Loading...
A lecture from IBM's Machine Learning with Python Course explaining the difference between supervised and unsupervised learning.

Tips to help ace your machine learning interview

The best way to ace an interview is to prepare for it 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, make sure to connect your answers with real-life examples, especially ones that reference your own work. Recruiters are usually looking for experience as well as 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’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. 

2. 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.

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 on Glassdoor.

4. 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 and fully explain your answers. You may be tested on your scientific rigor, so include charts, citations, and introduce notions when necessary.  For example, whether you're tasked with creating a simple model or a complex model, it's important to show every step of the process so your potential employer can see the work you've done.

5. 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 SAS' Machine Learning Rock Star, or hone your skills through Andrew Ng's three-course Machine Learning Specialization offered by Stanford and DeepLearning.AI. Upon completing each course or Specialization, you’ll also have a certificate to add to your resume or LinkedIn profile – a potential indicator of your skills and job preparedness.  

Placeholder

specialization

Machine Learning

#BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng

4.9

(7,899 ratings)

132,868 already enrolled

BEGINNER level

Average time: 3 month(s)

Learn at your own pace

Skills you'll build:

Decision Trees, Artificial Neural Network, Logistic Regression, Recommender Systems, Linear Regression, Regularization to Avoid Overfitting, Gradient Descent, Supervised Learning, Logistic Regression for Classification, Xgboost, Tensorflow, Tree Ensembles, Advice for Model Development, Collaborative Filtering, Unsupervised Learning, Reinforcement Learning, Anomaly Detection

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.

Big savings for your big goals! Save $200 on Coursera Plus.

  • For a limited time, save like never before on a new Coursera Plus annual subscription (original price: $399 | after discount: $199 for one year).
  • Get unlimited access to 7,000+ courses from world-class universities and companies—for less than $20/month!
  • Gain the skills you need to succeed, anytime you need them—whether you’re starting your first job, switching to a new career, or advancing in your current role.