14 Machine Learning Interview Questions (+ Tips to Answer Them)

Technical and programming interview questions are common for machine learning roles. At a glance, here's what you can expect from your machine learning interview:

• Recruiters want to assess your knowledge of fundamental machine learning methods and concepts like deep learning, natural language processing (NLP), and random sampling.

• 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. The interview gives you a chance to demonstrate that you know your stuff.

• 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.

To help you get started and build the confidence you need to ace your next interview, here are some of the most common questions you'll encounter. Afterward, if you're interested in brushing up on foundational machine learning skills, consider enrolling in the IBM Machine Learning Professional Certificate.

14 Machine learning interview questions

Here are 14 of the most common machine learning interview questions and explanations for answering them.

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 raw data. At the most basic level, it aims to understand your process and how you work.

How to answer: Explain the criteria you consider when evaluating different methods for handling missing or corrupted data. Factors like data distribution, underlying assumptions, computational efficiency, and the data set's specific requirements should be considered. Emphasize your ability to make informed decisions based on these criteria.

You may also want to provide a detailed account of the concrete steps you undertake in your data-cleaning process. This could include techniques such as exploratory data analysis, visualization, statistical tests, and the application of various imputation methods. Highlight your expertise in using specific tools, libraries, or programming languages.

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

The interviewer wants to know that you can explain the subtle differences between each concept to ensure that you have strong foundational knowledge. 

How to answer: When addressing the question about the difference between deep learning, artificial intelligence (AI), and machine learning, consider structuring your answer in a reverse funnel, starting with the high-level concepts first:

1. Start with a high-level definition: Provide a concise, general explanation of each concept to set the context for your answer.

2. Highlight the relationship between the concepts: Explain how deep learning and machine learning are subfields within the broader field of AI, emphasizing their interdependencies.

3. Discuss their applications and use cases: Provide examples of practical applications for each concept to illustrate their distinct uses and strengths.

4. Clarify the progression from AI to machine learning to deep learning: Explain how these concepts have evolved, with deep learning representing a more recent advancement within the field of machine learning.

3. Describe your favorite machine learning algorithm.

This question is an opportunity for you to show your preferences and individual skills while showing that you have a deep understanding of various common machine learning algorithms. Whether you enjoy 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 algorithms and their unique qualities.

Some common machine learning algorithms you might consider mentioning include:

• Linear regression

• Logistic regression

• Naive Bayes

• Decision trees

• Random forest

• K-nearest neighbor (KNN)

• K-means

How to answer: The exact algorithm you mention isn't as important as your reasons for selecting it. This question is an opportunity to draw direct comparisons to other algorithms, so it’s clear your expertise extends across many algorithms.

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. Which AI tools are you familiar with?

This question asks you to discuss your experience using AI-related tools in previous positions or schooling. Your interviewer is curious about your proficiency level with an assortment of AI tools that their company might use, so ensure you are discussing all levels of experience with any artificial intelligence software or programs you have encountered in the past.

How to answer: To best answer this question, you can talk about the tools you are most familiar with and elaborate on any specific tools you use that the company also works with. It is a good idea to research what tools the company uses ahead of time to find common ground.

5. What machine learning projects have you worked on previously?

In this case, the interviewer wants to know more about your experience completing machine learning projects, using different kinds of AI-related tools, and in what context you have used them. They are curious to learn more about your responsibilities and competencies regarding applying your skills and how well-suited you are to leading projects.

How to answer: To answer this question, consider your most impressive projects involving a wide range of AI and deep learning tools and extensive collaboration among team members. Demonstrating your work ethic and ability to complete projects can impress potential employers and give them a better understanding of your leadership skills.

6. 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 labeled and unlabeled training data sets and how they're used to create different types of machine learning models, such as classification models, linear regression models, discriminative models, and generative models. You might also consider highlighting any machine learning projects you have undertaken and explaining how you used supervised or unsupervised learning to accomplish them.

7. What is overfitting, and how do you prevent it?

When interviewers ask about overfitting and how to prevent it, they are typically assessing your understanding of a common challenge in machine learning and your knowledge of techniques to mitigate its impact. 

How to answer: Overfitting occurs when a machine learning model fits too exactly with its training data set and doesn't generalize well with new, unseen data. The opposite of overfitting is underfitting, which occurs when a machine learning model hasn’t been trained enough and performs poorly on training or new data. 

As you answer, be sure to offer a clear definition of overfitting, as well as discuss the following:

• Impacts of overfitting on model performance

• Causes and indicators of overfitting

• Methods to prevent overfitting

Consider framing your answer in terms of a real-world example. Discuss the steps you took and how they improved model performance or generalization.

8. What are false positives and false negatives? Why are they significant?

Not all errors that come from a machine learning model are the same. The consequences of those errors can be drastically different depending on the domain where the model is deployed. When asking this question, an interviewer wants to assess your understanding of the difference between type 1 (false positive) and type 2 (false negative) errors. Why might you optimize for one over the other?

How to answer: Provide a succinct definition of false positives and false negatives, explain their significance in the specific problem domain, and showcase your understanding of the trade-offs and potential strategies to minimize their occurrence. Be sure to discuss relevant examples or anecdotes to illustrate your comprehension of these concepts in real life.

9. What are some examples of supervised machine learning used in the world of business today?

Supervised machine learning is one of the most widely used methods for creating a machine learning model. Hiring managers want to ensure you clearly understand how such models are applied in the real world.

How to answer: Pick an application of supervised machine learning that speaks to both your expertise and the industry in which your potential employer operates. Choose examples you feel confident discussing in-depth with a hiring manager and can expand to include how they apply to the company's goals.

Examples of supervised learning

Common applications of supervised machine learning in business include customer churn prediction, credit scoring and risk assessment, fraud detection, image recognition, sentiment analysis, and demand forecasting.

10. What problems have you encountered in the deep learning field?

By asking you to identify a problem, your interviewer seeks to evaluate your critical thinking skills and ability to solve complex problems. They want to know how you deal with challenges and navigate difficult scenarios.

How to answer: To answer this question, you can think about a time you had a problem and how you approached it. You should explain how you solved—or did not solve—a problem related to deep learning.

11. Explain the difference between deductive and inductive reasoning in machine learning.

Machine learning models are built from machine learning algorithms trained on data sets. In effect, machine learning algorithms make assumptions about the world in much the same way we do: through either deductive or inductive reasoning. Hiring managers want to know whether you can explain their differences on the spot.   

How to answer: Explain that deductive reasoning in machine learning involves deriving specific conclusions or predictions from general principles or rules. It follows a top-down approach where the model applies predefined rules to reach specific outcomes.

Differentiate inductive reasoning by stating that it involves deriving general principles or rules from specific observations or examples. It follows a bottom-up approach where the model learns patterns and generalizes from the data to make predictions or decisions.

Emphasize that deductive reasoning typically requires pre-existing knowledge or explicit rules to apply to new data. In contrast, inductive reasoning focuses on learning from data to build models that generalize well to unseen examples.

12. How do you know when to use classification or regression?

Knowing when to use classification or regression models is crucial in machine learning. These two algorithms serve distinct purposes, and understanding their suitability for different problems is essential for effective modeling.  

How to answer: Classification models are the go-to choice when the task involves labeling or categorizing new data instances. For instance, consider an application that identifies different types of plants based on their pictures. On the other hand, regression models are employed when the goal is to predict an outcome that is either a variable quantity or the probability of a binary classification. 

Provide concrete examples from your own work experience to illustrate your proficiency. For instance, you can mention a project where you developed a classification model to categorize customer feedback into sentiment categories, enabling sentiment analysis for a product or service. Alternatively, discuss a regression model you built to predict customer churn probability based on various customer attributes, helping the business proactively retain valuable customers.

13. Explain how a random forest works.

Understanding how a random forest works often involves knowledge of decision trees, feature selection, ensemble methods, and metrics used for model evaluation. The interviewer can assess your knowledge and familiarity with these related topics by asking this question. 

How to answer: When answering this question, explain the random forest algorithm clearly and concisely, including its key components and steps. Explain the process of building decision trees, the concept of bootstrapping and feature randomness, and the ensemble aggregation mechanism. Additionally, discuss the advantages of random forests, such as handling high-dimensional data, mitigating overfitting, and providing feature importance rankings.

Consider providing examples of how you have utilized random forests in your previous work or academic projects. Illustrate your understanding of parameter tuning, model evaluation, and any insights from using random forests in real-world scenarios.

14. Discuss frameworks you’ve utilized for deep learning applications.

If an interviewer asks you to discuss deep learning frameworks you’ve used in the past, discuss your experience to show them what frameworks you are comfortable with and if you have expertise in any of them.

How to answer: You can detail your past experiences to demonstrate what kind of worker you are and how familiar you may be with different tools and applications. This is a good opportunity for you to expand on anything that might not be on your resume and impress the interviewer with your technical competencies.

Tips to ace your machine learning interview

The best way to ace an interview is to prepare, prepare, prepare. Aside from practicing the above interview questions, here are some additional tips to help make a great impression:

1. Tie theoretical concepts to real-world scenarios.

Throughout your interview, make sure to connect your answers with real-life examples, especially ones that reference your work. Hiring managers want to know that you've had experience with these concepts and that you know how to explain or persuade teams.

It’s also beneficial to show that you are always learning and developing your skills. During the interview, show how driven you are to improve yourself and your expertise.

2. Focus on what you know.

Each candidate has unique strengths and experiences in machine learning. Highlight your specific strengths, such as expertise in a particular algorithm, proficiency in data preprocessing, or experience with a specific domain. This helps you stand out and differentiate yourself from other candidates.

3. Research the company. 

Knowing machine learning is important, but how will your specific skills and experience benefit the company? To demonstrate your excitement, you'll want to be familiar with the company's mission and values, previous work, and current products. You can tailor your responses toward how and why you are the right person to take on this role.

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. Prepare to code.

While the specific format and requirements can vary depending on the company and position, it is common for machine learning interviews to include coding exercises or technical assessments.

Prepare for coding challenges by practicing coding exercises, implementing machine learning algorithms, and familiarizing yourself with common libraries or frameworks used in the industry, such as TensorFlow, scikit-learn, or PyTorch. Additionally, understanding the underlying mathematics and theory behind machine learning algorithms will help you in effectively implementing and explaining your code during the interview.

Get job-search ready with Coursera

Machine learning professionals are in demand. Prepare for your next search with these popular job search resources from Coursera:

• Watch on YouTube: Write Better Cover Letters with AI

• Read more: Machine Learning Interview Prep Guide

• Explore: Popular Machine Learning Courses and Certifications

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