Can you list some machine learning use cases which interest you?

"One of the reasons I became interested in machine learning is because it can be applied to so many different disciplines. Some use cases I've been most fascinated with include dynamic pricing, personalized marketing, process automation, and fraud detection. After researching your organization, I believe each one of these applies to your operations and some of the challenging problems your team is trying to solve."

There are several reasons an interviewer will ask this type of question. The first is to understand your interests in machine learning and the type of topics you follow. The second is to see if your interests align with those of the organization and the type of work they do. This is important because organizations look for individuals with both talents and interests related to the job. Finally, as with most questions, the interviewer is looking to see how well you communicate your interests in simple, nontechnical language.

What experience do you have performing research in the field of machine learning?

"In several of my previous positions, I worked with senior machine learning experts on research projects related to artificial intelligence. I was listed as an author of several publications about artificial intelligence, augmented reality, and other machine learning disciplines. Details of this are documented in my resume."

In addition to learning about your qualifications as a machine learning engineer, organizations are interested in how you may have contributed to the technology. Candidates who have done research, published papers, conducted studies, or otherwise enhanced machine learning knowledge will have an advantage over those who simply perform work in this field.

What are the most recent publications, papers or articles you have read about machine learning topics?

"Some interesting articles I read recently about the science of machine learning include Google's launch of Cloud AI Platform Pipelines, how AI implants gave amputees control over prosthetic limbs, published by the MIT Technology Review, and how AI is changing the video game industry through augmentation and synthetic media. Each of these demonstrated the broad application of machine learning and the diversity of how companies are using this technology to enhance the user experiences with their products."

The field of machine learning evolves quickly, with new developments and changes occurring almost daily. Keeping up with the developments in this field is a key responsibility of a machine learning engineer. Interviewers will ask this question to confirm that you are making efforts to stay current in the technology. You should be prepared to list two or three recent publications you have read and be able to summarize their contents if asked to.

Can you talk about how precision and recall are used in the work you do?

"Precision, also known as the positive predictive value, measures the number of positives an algorithm claims compared to the actual number of positives in the data. Recall, which is sometimes described as the true positive rate, is a similar measure but discounts data that doesn't fit the model. An example of this is if you predicted 20 girls and 5 boys in a class that actually consisted of only 20 girls, your recall rate would be 100% because there were 20 girls and the class. On the other hand, your precision would only be 80% since 5 of your predictions were wrong."

During an interview, you will be asked about various terms, concepts, processes and procedures you use in your job. The interviewer may ask you to define them, contrast them, or give examples of how you use them. While it is good to be familiar with as many of these as possible, you may be asked about topics you don't use regularly or are unfamiliar to you. If this occurs, you should acknowledge that you are unfamiliar with the concept and then describe how you would learn about it if it is important for the job.

Do you have a "˜go-to' algorithm, and can you describe it to me?

"My favorite type of algorithms involve regression analysis. The process they use is to look at the way data performed in the past and use this to predict future trends. Along these, my favorite is Decision Forest Regression. This type of algorithm is both accurate and requires little training time for the users."

The purpose of this question is not to understand your favorite algorithm but rather to see how you communicate and if you're able to explain complex topics in simple language. During most interviews, you will be speaking with someone familiar with the job's technicalities for which you are interviewing. However, you may interview with someone from the personnel department or other business units within the company on some occasions. Being able to explain complex concepts in simple, easy-to-understand language demonstrates your ability to work cross-functionally in the organization.

In your opinion, what is the most valuable data applicable to our business?

"Since you are one of the leading organizations in the transportation industry, the most valuable data you can use to manage your business involves the public's use of transportation, their preferences, seasonal fluctuations and the use of various modes of transportation. It would also be useful to know how transportation providers coordinate their activities to create an efficient network."

This is a general question which the interviewer will use to begin the conversation, learn more about your background, and collect information they can use throughout the interview. This question assumes that you've done some research on the company and industry and can provide specific information relevant to their business. When preparing for an interview, you should find out as much as you can about the organization, the position you are interviewing for, and the interviewer's background. This will help you anticipate the questions you will be asked and provide the information you need to respond to them.

Can you talk about deep learning and how it compares to other machine learning algorithms?

"Deep learning is a subset of machine learning. It is focused on neural networks and how to leverage principles from neuroscience to better model unlabeled and semi-structured data. The algorithms employed in deep learning classify data through the use of neural networks."

This is a general question related to the field of machine learning. While it has some technical aspects, the interviewer uses it to better understand your communication style and your ability to discuss technical terms in simple, easy to understand language. The trick to these types of questions is not to overcomplicate them and spend too much time answering them. As with most interview questions, be brief and to the point, will serve as your best strategy. The interviewer will ask you a follow-up question if they need additional information.

What steps would you use to create and implement a data-based decision-making system for our company's users?

"When creating a data-based decision-making system, the first thing I do is interview the users to understand the problems they are trying to solve. I then research the sources of information that are relevant to the company's operations. Once I understand these, I create a system that incorporates databases, artificial intelligence tools, and machine learning principles. The final step is to develop a user interface which enables the employees to quickly access the information they need."

This is an operational question that the interviewer uses to better understand how you perform your job. The best way to respond to an operational question is to break down the processes you use into individual steps and briefly describe them in the order in which you execute them. Your answer to an operational question should be brief and to the point. You should also anticipate follow-up questions.

Bayes' Theorem is often described as 'Naive.' Why is Bayes naive?

"Even though the Bayes' Theorem has many practical applications, it is considered naive because it assumes conditions that are not indicated by the data. This is due to the conditional probability being calculated as a product of the components' individual probabilities. The result is an assumption of independence of the features, which could not occur in the real world."

This is a follow-up to the question 'Can you define Bayes' Theorem and discuss how it is useful in the context of machine learning?'. Anytime you provide an answer during an interview, you can anticipate a follow-up question. This indicates that the interviewer would like to explore the topic in more depth or that the subject being discussed is important to the organization. Keeping your original answers brief and to the point will encourage follow-up questions. This also helps you to focus on what is important in all of your answers.

Please discuss the purpose of regularization and explain the difference between L2 and L1 regularization.

"The purpose of regularization is to spread error amongst all the component data. The difference between L2 and L1 one regularization is that L2 spreads the error among all of the components, while L1 is more binary and tends to assign either a one or zero weighting to each term."

Another technical question asking you to discuss the differences between two terms or concepts used in the field of machine learning. As the interview progresses, so you'll continue to be asked technical questions. However, their complexity will continue to increase. This indicates that the interviewer is gaining confidence in your abilities and is willing to explore more difficult areas to understand the depth of your expertise.

Can you define Bayes' Theorem and discuss how it is useful in the context of machine learning?

"Bayes' Theorem provides you the subsequent probability of an outcome based on prior knowledge. The mathematical formula is expressed as the true positive rate of a sample divided by the sum of the population's false positive rate and the true positive rate of the sample. It is used to predict the probability of a theoretical outcome."

You probably already recognize this as a technical question since it asks you for a definition and then requests that you discuss the term in the context of a machine learning engineer's role. Keep in mind that when answering technical questions, you should be brief and to the point. This encourages the interviewer to either move on to the next topic or ask a follow-up question if they have a specific interest in this area. Pursuing this strategy will help you define what is important to the interviewer so you can begin to tailor your answers to better match their needs and the position's requirements.

What's the difference between the concepts of probability and likelihood?

"While probability and likelihood are similar in many ways, the key difference is that probability is associated with the results you obtained while likelihood is associated with the theorem. Likelihood defines the chance that your hypothesis resulted in the data you obtained. Probability, on the other hand, describes the chance that your Theorem is true based on the data you had."

This is a challenging technical question in that it asks you to compare two very similar but different concepts used in machine learning. It is more difficult to compare two similar items than it is to compare two completely different items. Being able to communicate the nuances between these two concepts will not only demonstrate your technical proficiency but will also provide the interviewer would a good idea about your communication skills.

Can you explain the difference between Type I and Type II errors?

"Type I errors indicate a false positive, while Type II errors indicate a false negative. Another way to understand this is that a Type I error will say something has happened even though it hasn't, while a Type II error is just the opposite."

This is a very basic technical question which most machine learning professionals and anyone who has taken a class in statistics can answer. The type of technical questions you will be asked during an interview will range from very simple to very difficult. This depends on the interview stage, the position for which you are interviewing, and the hiring manager's knowledge. Regardless of the question's complexity, your answer should still be brief and to the point, and you should anticipate follow-up questions.

Please define a Fourier transform and discuss how it is used?

"A Fourier transform is a method used to deconstruct basic steps to understand the overall function or process. It is typically used to understand processes involving frequencies, amplitudes, or cycles. An easy way to understand it is trying to determine the recipe used to make an entr?e in a restaurant."

This is a classic technical question that asks you first to define a concept and then discuss its use. When preparing for an interview, you can locate a glossary of terms used in your profession, review their definition, and then formulate a description of how they are used in the work you do. Practicing your answers to the questions out loud will make it easier for you during the actual interview.

Please discuss how a ROC curve works.

"A ROC curve provides a graphical picture of the difference between the true positive rates and the false positive rate at various thresholds. This helps the user to better understand the differences between the sensitivity of the model, represented by the true positives, and the probability of the model providing inaccurate information, also known as false positives."

This technical question assumes that you know the definition of the concept the interviewer is questioning you about, and you can describe how it functions. As with any technical question, your answer should begin with a brief definition of the term, followed by a description of how it is used in work done by a machine learning engineer. You may or may not want to include an example. You should also anticipate follow-up questions, which indicate that the interviewer has a strong interest in this topic or that it is important to their work.

Please discuss the differences between generative and discriminative models?

"Both generative and discriminative models are related to categories of data. A generative model is designed to learn the categories of the data in a study, while the discriminative model seeks only to learn the distinction between the categories. In machine learning, discriminative models outperform generative ones when conducting classification exercises."

You probably already recognize this as a technical question. Questions asking you to discuss the differences between two concepts or terms used in your profession are easily identified as technical. The best way to respond to a technical question is to first define the terms, then either compare them or discuss how they are used in your work. You may also want to provide some examples relevant to the organization you are interviewing with to further demonstrate your knowledge in this area.

What is the difference between K-Nearest Neighbors and k-means clustering?

"The main difference between K-Nearest Neighbors and K-means clustering is that K-Nearest Neighbors is a supervised classification algorithm, while K-means is unsupervised. Supervised classification algorithms label the data and assign it to groups, while unsupervised ones do not. K-Nearest Neighbors uses the labeled data to classify an unlabeled point. K-means clustering will learn how to cluster unlabeled points by computing the means of the distance between different points."

When interviewing for a role as a machine learning engineer, you will be asked a wide range of technical questions related to the field. The best way to prepare for this is to review the concepts, terms, processes, and procedures you use in your job. You should also carefully read the job description and research the organization to understand the type of work it does. Finally, practicing questions like these will help you become familiar with the type of question you will be asked and the best way to respond to them.

Can you discuss the difference between supervised and unsupervised machine learning and when each one is used?

"The key difference between supervised and unsupervised machine learning is the labeling of the data. Supervised learning labels the data to train the model to place the data in a specific group. Unsupervised learning does not require the data to be labeled and groups it all together. The type of learning you use depends on the model you are creating and the objective of the study."

You should recognize this as a technical question since it asks you to compare two concepts used in a machine learning engineer's work. When responding to this type of question, you should first define each concept and then compare the differences or similarities. Providing the interviewer with an example relevant to their business operations will strengthen your answer.

Can you briefly discuss the trade-offs between bias and variance?

"Bias error is usually the result of over-simplifying your assumptions in a learning algorithm. This reduces the predictive accuracy of the model. Variance, on the other hand, results from too much complexity in the learning algorithm. This results in the algorithm dismissing important data, and your results being skewed. The key to developing a good learning algorithm is to use a balance between bias and variance."

This is an example of a technical question. When interviewing for a machine learning engineer's role, the majority of the interview will consist of technical questions. Like operational questions, technical questions are best answered directly and succinctly. You begin by defining the terms addressed in the question and, in this case, compare them. You may also want to give an example of how you use the concept in your work.

What is the purpose of pruning a decision tree?

"Pruning a decision tree refers to the process of removing branches that have weak predictive outcomes to reduce the complexity of the model and increase the accuracy of the decision tree. Approaches to this include reduced error pruning and cost complexity pruning, both of which can be performed either top-down or bottom-up. The process involves removing a branch and then testing the model to determine if the accuracy was increased or remained the same. The branch can be reinserted if its removal does not affect the accuracy of the model."

Anyone outside of the field of machine learning may not understand this question. It appears to be more agriculture rather than machine learning related. However, as an engineer in this field, you should immediately recognize the concept and be able to discuss it. Since this is a technical question, keep your answer brief and to the point. You should also anticipate follow-up questions, indicating that this is an important process used by the organization.

Machine Learning Interview Questions

Go Back

1. Can you list some machine learning use cases which interest you?
2. What experience do you have performing research in the field of machine learning?
3. What are the most recent publications, papers or articles you have read about machine learning topics?
4. Can you talk about how precision and recall are used in the work you do?
5. Do you have a "˜go-to' algorithm, and can you describe it to me?
6. In your opinion, what is the most valuable data applicable to our business?
7. Can you talk about deep learning and how it compares to other machine learning algorithms?
8. What steps would you use to create and implement a data-based decision-making system for our company's users?
9. Bayes' Theorem is often described as 'Naive.' Why is Bayes naive?
10. Please discuss the purpose of regularization and explain the difference between L2 and L1 regularization.
11. Can you define Bayes' Theorem and discuss how it is useful in the context of machine learning?
12. What's the difference between the concepts of probability and likelihood?
13. Can you explain the difference between Type I and Type II errors?
14. Please define a Fourier transform and discuss how it is used?
15. Please discuss how a ROC curve works.
16. Please discuss the differences between generative and discriminative models?
17. What is the difference between K-Nearest Neighbors and k-means clustering?
18. Can you discuss the difference between supervised and unsupervised machine learning and when each one is used?
19. Can you briefly discuss the trade-offs between bias and variance?
20. What is the purpose of pruning a decision tree?

Machine Learning Interview Questions

1. Can you list some machine learning use cases which interest you?

2. What experience do you have performing research in the field of machine learning?

3. What are the most recent publications, papers or articles you have read about machine learning topics?

4. Can you talk about how precision and recall are used in the work you do?

5. Do you have a "˜go-to' algorithm, and can you describe it to me?

6. In your opinion, what is the most valuable data applicable to our business?

7. Can you talk about deep learning and how it compares to other machine learning algorithms?

8. What steps would you use to create and implement a data-based decision-making system for our company's users?

9. Bayes' Theorem is often described as 'Naive.' Why is Bayes naive?

10. Please discuss the purpose of regularization and explain the difference between L2 and L1 regularization.

11. Can you define Bayes' Theorem and discuss how it is useful in the context of machine learning?

12. What's the difference between the concepts of probability and likelihood?

13. Can you explain the difference between Type I and Type II errors?

14. Please define a Fourier transform and discuss how it is used?

15. Please discuss how a ROC curve works.

16. Please discuss the differences between generative and discriminative models?

17. What is the difference between K-Nearest Neighbors and k-means clustering?

18. Can you discuss the difference between supervised and unsupervised machine learning and when each one is used?

19. Can you briefly discuss the trade-offs between bias and variance?

20. What is the purpose of pruning a decision tree?