Machine Learning Interview Questions (+ Tips to Answer Them)

How to prepare for a machine learning interview

Technical and programming interview questions are common for machine learning roles. Recruiters want to assess your knowledge of fundamental machine learning methods, and concepts such as deep learning (DL), 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. To build the confidence you need to ace your next interview, you can learn about common questions you may encounter. You can use these to practise and get good at answering them in an interview setting.

10 machine learning interview questions

During an interview for a machine learning role, the hiring team will likely ask various questions about your experience, previous projects, and foundational knowledge of machine learning. The list below includes some of the questions you may need to answer to demonstrate your technical proficiency and problem-solving skills.

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. Take into account factors like data distribution, underlying assumptions, computational efficiency, and the specific requirements of the data set. Emphasise 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, visualisation, statistical tests, and applying 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. Begin by providing 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, emphasising 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 over time, with deep learning representing a more recent advancement within the field of machine learning.

3. Describe your favourite 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 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 neighbour (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 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.

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 labelled 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 either supervised or unsupervised learning to accomplish them.

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

When an interviewer asks 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 generalise well with new, unseen data. The opposite of overfitting is underfitting, which occurs when a machine learning model has not gone through enough training and doesn't perform well on training data or new data. 

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

• 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 specific steps you took and how they resulted in improved model performance or generalisation.

6. 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 you choose to deploy the model. 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 optimise for one over the other?

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

7. 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 make sure you have a clear understanding of how such models are applied in the real world.

How to answer: Pick an application of supervised machine learning that speaks to both your own expertise, and also the industry in which your potential employer operates. Choose examples that you feel confident discussing in-depth with a hiring manager and that you can expand to include how it applies 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.

8. 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 generalises from the data to make predictions or decisions.

Emphasise 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 generalise well to unseen examples.

9. 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 types of algorithms serve distinct purposes, and understanding their suitability for different problems is essential for effective modelling.

How to answer: Classification models are the go-to choice when the task involves labelling or categorising 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 categorise 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.

10. 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. By asking this question, the interviewer can assess your knowledge and familiarity with these related topics. 

How to answer: When answering this question, provide a clear and concise explanation of the random forest algorithm, 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 utilised random forests in your previous work or academic projects. Illustrate your understanding of parameter tuning, model evaluation, and any insights gained from using random forests in real-world scenarios. 

Tips to pass your machine learning interview

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

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 own work. Hiring managers want to know that you've had experience with these concepts and you know how to explain or persuade teams.

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.

Focus on what you know.

Each candidate has their own 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.

Research the company. 

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

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.

Prepare to code.

Whilst 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 practising coding exercises, implementing machine learning algorithms, and familiarising 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.

Next steps for your career

Machine learning interviews assess your understanding of fundamental concepts like deep learning, NLP, and random sampling, requiring you to demonstrate problem-solving skills and technical knowledge. Preparing for common interview questions can help you stand out and show why you're an ideal candidate for roles such as machine learning engineer or data scientist.

Brush up on your machine learning expertise with a self-paced course from an industry leader. Develop a foundation of AI concepts and practical machine learning skills with Machine Learning from DeepLearning.AI, available on Coursera. Upon completion, you’ll also have a certificate to add to your resume or LinkedIn profile to indicate your skills and expertise.