How To Prepare for Machine Learning Interview Questions


Changing careers can seem daunting, but it’s the interview process that’s the most nerve-wracking. It’s a normal feeling to have. But with preparation and practice, you can put your best foot forward during your interviews.

To help you prepare, we’ll explore some of the most common machine learning interview questions in the paragraphs below. Along with the questions, we’ll also provide some tips for how to practice and explain what you can expect during your machine learning interview.

What to expect in a machine learning interview

Whether it’s all virtual or some portions are in person, there will be a live aspect to your interview. Hiring managers and recruiters like to see how their potential new employees can communicate and perform under a little bit of pressure. It’s also a great chance to see if both manager and employee personalities are a good fit.

All the same standard advice applies to virtual interviews as those that occur in person. Be sure to dress professionally and in line with the company’s dress code. Show up a few minutes early so that you’re ready to go. This will also give you time to sort out any software or connectivity issues.

Before delving into the technical aspects of machine learning, your interviewer may warm up with questions about your experience and passion for the field. These questions may include:

  • What role do you think data plays in our business?
  • Can you share how you resolved a programming problem recently?
  • How do you stay up to date on the latest in the field of machine learning?
  • What excites you the most about a career in machine learning?
  • Where do you think machine learning is underutilized in our industry?

Let your personality and interests shine through when answering these questions. You may even strike up an interesting conversation with your future boss. Don’t be shy. Show you know what you’re talking about and that you’re happy to be there. Being professional doesn’t mean you need to be emotionless.

Common machine learning interview questions

During a machine learning interview, the questions you’ll face will test your familiarity with the field’s technical and conceptual aspects. Use this list of common questions to prepare for your machine learning interview:

  • Describe/differentiate between the terms: machine learning, artificial intelligence, and deep learning
  • How are bias and variance related?
  • How are Type I and Type II errors different?
  • Can you describe what “overfitting” is?
  • Describe your favorite machine learning algorithm
  • What’s the difference between supervised learning and unsupervised learning?
  • How are generative and discriminative models the same? How are they different?
  • How do you prune a decision tree?
  • How would you evaluate the effectiveness of your machine learning model?
  • Have you ever worked with a missing or corrupted dataset? How did you handle it?
  • What is a hash table?
  • How do you prefer to visualize your results? What tools do you use?
  • Name three machine learning algorithms
  • What data types does JSON support?
  • In SQL, how are primary and foreign keys related?

While the questions listed above are a great start, you might also want to consider reaching out to someone who works for the company you’re applying to and asking about their skills, tools, and daily responsibilities. This will help you figure out what to emphasize during your interview.

Tips and tricks for answering machine learning interview questions

The best way to prepare for the questions you’ll face in your machine learning interview is to rehearse them beforehand. You could enlist the help of a friend for this step, and they don’t even need to be as well-versed in machine learning as you are. For instance, you could have the questions with answers printed out for them on scrap sheets of paper, or they could quickly Google the answer to verify it’s correct.

If you’ve ever practiced for a speech or presentation, then you know the best way to prepare is to talk it out just like you would during the real deal. Doing so helps you collect your thoughts and ensures that you’re able to speak clearly and confidently during your interview. Remember, getting the answer right is only one piece of the pie — you also need to communicate effectively.

After you’ve practiced answering the machine learning questions above, there are two other tips you can employ to get ready for your interview. The first is to keep coding. Practice your machine learning skills by continuing to work on projects or by taking a machine learning course. There’s no better way to cement concepts into your mind than through application.

The second tip is to remember that not knowing the answer isn’t the end of the interview. You can be honest. Say, “I’m not sure the exact answer off the top of my head, but here’s how I’d find out…”

Knowing where and how to find answers is a skill that recruiters want to see, especially in a machine learning expert. There will always be new things to learn and concepts you’ll need to read up on before implementation. Don’t get flustered. Just be comfortable knowing you’ll never know everything.

How to get started with machine learning

If you’re not quite at the interview stage yet and want to learn more about the field before pursuing a career, check out our introductory course on machine learning. Once you’ve learned the basics, the next step is to learn how to use programming languages like Python or R.

If you already have a couple of languages under your belt and want to learn how to apply them to machine learning, check out any of the skill paths below:

And if you want to make sure all your bases are covered and you’re equipped with all the skills you’ll need to land a job in machine learning, try our Machine Learning/AI Engineer or Data Scientist: Machine Learning Specialist career path.

Machine Learning Courses & Tutorials | Codecademy
Machine Learning is an increasingly hot field of data science dedicated to enabling computers to learn from data. From spam filtering in social networks to computer vision for self-driving cars, the potential applications of Machine Learning are vast.

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