Types of Machine Learning Interviews and how to ace them (2023)

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A detailed guideline on different categories of machine learning interviews and how to prepare for them. Preparation resources: How to Prepare for Coding Interviews? A guide of timeline and resources for your coding interview preparation. Preparation resources: Machine Learning 4,499,885 already enrolled Machine learning is the science of getting computers to act without being explicitly… Machine Learning Interview Questions This page will guide you to brush up on the skills of machine learning to crack the interview. Here, our focus will be… 51 Machine Learning Interview Questions with Answers | Springboard Machine learning interview questions are an integral part of the data science interview and the path to becoming a data… Cheat Sheets for Machine Learning Interview Topics A visual cheat sheet for ML interviews (www.cheatsheets.aqeel-anwar.com) Preparation resources: Machine learning case study interview AI organizations divide their work into data engineering, modeling, deployment, business analysis, and AI… How Would You Improve Google Maps | PM Exercises First off, I'd like to outline my understanding of the product to make sure I fully understand all the important… How can you improve Facebook Stories? | PM Exercises I would focus on improving new user experience for FB stories and help user learn about this feature, use this feature… How would you design a bicycle renting app for tourists? | PM Exercises The aim is to design a bicycle renting app for tourists. Locals and tourists can pick-up and drop their rented bicycles… How would you measure the success of Uber Ride? | PM Exercises Question: How would you measure the success of Uber Ride? Preparation respources 5 Common System Design Concepts for Interview Preparation - GeeksforGeeks In the software engineering interview process system design round has become a standard part of the interview. The main… System design fundamental concepts I have tried to covered fundamental concepts, topics and bits and peaces when preparing for system design interview… Design the Uber backend: System design walkthrough This question asks you to create a ride-sharing service to match users with drivers. This tutorial will break down this… Top 10 system design interview questions for software engineers The system design interview is an important factor in the hiring process. Today, we explore the top 10 SDI questions… Cracking system design interviews guide Overview Method Questions Concepts Prep Practice system design 1-on-1 with ex-interviewers. Learn more. System design… FAQs Videos

Types of Machine Learning Interviews and how to ace them (1)

A detailed guideline on different categories of machine learning interviews and how to prepare for them.

In the Spring of ‘21, I started applying for jobs. I couldn’t find one concise article on what are the different aspects that I will be interviewed on. In the initial interview series, at times I was clueless. I had to learn it the hard way that no matter how good a researcher or ML engineer you are when it comes to interviews that are not enough. You must prepare for it! Before you can even start preparing, you need to know what are the different areas that you will be tested on.

In this article, I will discuss the different types of Machine learning interviews based on the 58 hours of interviews I have had earlier this year. The pie chart below summarizes the different categories of Machine Learning Engineer/Data scientist interviews.

Types of Machine Learning Interviews and how to ace them (2)

We will focus on screening, coding, machine learning, case study, and system design.

This interview is rather casual, and most often the first step into the series of interviews. Its normally conducted by a recruiter or a hiring manager. The main purpose of this interview is to give the candidate an idea of the company, job description, and responsibilities. The candidate is also asked about his/her background in a casual setting to see if the candidate’s field of interest aligns with the job.

Nature: Non-technical

Mode: Normally a Phone Call

Duration: 15–20 mins

Preparation: You should be able to explain your experience in a few mins.

Coding/programming is an important part of the machine learning interviews, and more often than not are used to filter out candidates before moving them forward to the machine learning-related interviews. Good programming skills are required to perform in such interviews. Coding interviews usually last about 45–60 mins and consist of two questions. The interviewer explains the problem and expects the candidate to solve it in optimum time and space complexity.

Preparation resources:

Preparation of such interviews requires a good understanding of data structures, time-space complexities, the ability to understand the problem, and good time management skills. Following are some good resources

How to answer a Coding Interview Question?A step-by-step guide to help you answer a coding question.towardsdatascience.com
(Video) Machine Learning Interview Questions
Seven (7) Essential Data Structures for a Coding Interview and associated common questionsImportant data structures visualized with animationstowardsdatascience.com

Machine learning interviews assess your knowledge related to Machine Learning. Based on the job requirements, the topics can include supervised learning, unsupervised learning, reinforcement learning, convolutional neural networks, recurrent neural networks, generative adversarial networks, natural language processing, etc.

Preparation resources:

Make sure you go through the job requirements and identify specific topics of ML that are required. If nothing specific is mentioned, then you can focus on the basics.

a. An in-depth course on ML: If you have not formally taken a course on ML during your studies, I would strongly suggest you take one online. Among the various ones available online, I would recommend Andrew Ng’s Machine Learning course offered by Stanford available on Coursera.

b. Refresher Articles: If you have already taken such a course, then going through a few refresher articles on ML will be really helpful for you to create an in-depth understanding of ML topics. A small list (10) of such articles can be found below.


c. Sample interview questions: Getting an idea of what usually is asked in ML interviews helps gauge your preparation. Such example questions can be found below. Make sure you go through them AFTER you have done your preparation.

d. Cheatsheets: It's always good to have cheatsheets that you can go through the night before your interview. Below is the link to an ML cheatsheet

These are usually open-ended questions aimed at analyzing the candidate's ability to have meaningful ML discussions on the overall project management and project acumen. An open-ended problem is presented by the interviewer, such as “How would you improve google maps?”. Such open-ended questions can result in a very chaotic answer if not approached properly.

Preparation resources:

A good answer template goes a long way in approaching such questions. Try to organize your answer using the following template

  1. Listen to the question
  2. Describe the product and its mission
  3. Ask clarifying questions
  4. State your assumptions
  5. Identify the pain points
  6. Identify solutions to the pain points
  7. Compare solutions through the table
  8. Discuss the KPI
  9. Summarize

Following is a good resource in understanding case study questions in depth

Some examples of such open-ended questions can be found below

These interviews assess the candidate’s ability to design an end-to-end scalable system for the underlying problem. Most of the engineers are so focused on a bug that they forget or at times even fail to realize the bigger picture. A system design interview requires knowledge of various components that come together to create a scalable solution to the problem. These components include front-end design, load-balancer, database sharding, caching, proxies, SQL, API, etc. A good understanding of these topics helps design an efficient and scalable end-to-end system.

Preparation respources

  1. Components and concepts:
The complete guide to crack the System Design interviewTemplate to answer any system design question — Links to all the resources attached.towardsdatascience.com

2. Examples Questions and Solutions

Machine learning interviews these days are much more than just questions on basic ML topics. They also include open-ended questions, case studies, coding, system design, etc. Understanding different categories of machine learning interviews can help the candidate be informed and prepare accordingly. In this article, we went through in detail on five most common ML interview categories and how to prepare for them.

If this article was helpful to you or you want to learn more about Machine Learning and Data Science, follow Aqeel Anwar, or connect with me on LinkedIn or Twitter. You can also subscribe to my mailing list.


How do I clear my machine learning interview? ›

  1. 7 Tips To Crack Data Science And Machine Learning Interviews! 7 Detailed Tips To Help You Perform Better In Data Science And Machine Learning Interviews. ...
  2. Hone Your Basics. ...
  3. Be Confident. ...
  4. Own Up To Your Resume. ...
  5. Show Your Passion. ...
  6. Focus On Specifics. ...
  7. Strong Honest Attitude. ...
  8. Make It A Habit To Make Brief Notes.
29 Mar 2021

What are machine learning interviews like? ›

A Machine Learning interview calls for a rigorous interview process where the candidates are judged on various aspects such as technical and programming skills, knowledge of methods, and clarity of basic concepts.

How do you prepare for machine learning? ›

My best advice for getting started in machine learning is broken down into a 5-step process:
  1. Step 1: Adjust Mindset. Believe you can practice and apply machine learning. ...
  2. Step 2: Pick a Process. Use a systemic process to work through problems. ...
  3. Step 3: Pick a Tool. ...
  4. Step 4: Practice on Datasets. ...
  5. Step 5: Build a Portfolio.

How do I stop overfitting? ›

Early stopping

Measuring the performance of your model during the training phase through each iteration is a good technique to prevent overfitting. You can do this by pausing the training before the model starts to learn the noise.

How do you tackle overfitting and Underfitting? ›

Using a more complex model, for instance by switching from a linear to a non-linear model or by adding hidden layers to your neural network, will very often help solve underfitting. The algorithms you use include by default regularization parameters meant to prevent overfitting.

What is bias in ML? ›

What is bias in machine learning? Bias is a phenomenon that skews the result of an algorithm in favor or against an idea. Bias is considered a systematic error that occurs in the machine learning model itself due to incorrect assumptions in the ML process.

What are the different types of machine learning? ›

These are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

What is regularization in ML? ›

What is Regularization in Machine Learning? Regularization refers to techniques that are used to calibrate machine learning models in order to minimize the adjusted loss function and prevent overfitting or underfitting.

What are bagging and boosting? ›

Bagging and Boosting are two types of Ensemble Learning. These two decrease the variance of a single estimate as they combine several estimates from different models. So the result may be a model with higher stability.

What is outliers in machine learning? ›

Outliers are those data points that are significantly different from the rest of the dataset. They are often abnormal observations that skew the data distribution, and arise due to inconsistent data entry, or erroneous observations.

What are feature selection techniques in machine learning? ›

Feature Selection is the method of reducing the input variable to your model by using only relevant data and getting rid of noise in data. It is the process of automatically choosing relevant features for your machine learning model based on the type of problem you are trying to solve.

How does machine learning handle duplicate data? ›

1) Use the get_duplicate_features functions to get all the constant features. 2) Store all the duplicate features as a list for removing from the dataset. 3) Drop all such features from the dataset.

How do you choose the right note while constructing a decision tree? ›

How do you choose the right node while constructing a decision tree? (C) An attribute having the lowest information gain. (D) An attribute having the highest information gain. Explanation: We select first those attributes which are having maximum information gain.


1. Impact of adding more variables to model. Deep Learning Interviews Question 1. ML Interviews
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2. How to prepare for Machine Learning interviews- Part 1 | Applied AI Course
(Applied AI Course)
3. SDS 615: How to Ace Your Data Science Interview — with Nick Singh
(Super Data Science Podcast with Jon Krohn)
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5. how programmers overprepare for job interviews
(Joma Tech)
6. Ace the Data Science Interview with Nick Singh
(Data Science Dojo)
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