Machine Learning Basic Questions in depth.
Machine Learning Engineer Interviews
Machine Learning Engineer Interview Questions
Companies rely on machine learning engineers to help design and improve the systems that allow their software to improve on its own, rather than being specifically programmed. During the interview process, be prepared to be tested heavily on both computer science and data science knowledge with an emphasis on recognizing patterns and trends. A bachelor's degree in computer science or a related field will be required.
Top Machine Learning Engineer Interview Questions & How to Answer
Question 1
Question #1: What are the most important algorithms, programming terms, and theories to understand as a machine learning engineer?
How to answer
How to answer: Be prepared to talk about things like Type I and Type II errors, supervised and unsupervised machine learning, ROC curves, and other key parts of machine learning. Employers want to know you have a strong knowledge of the technical aspects of the job position.
Question 2
Question #2: How would you explain machine learning to someone who doesn't understand it?
How to answer
How to answer: Sometimes machine learning engineers have to work with people who aren't familiar with the technical aspects of the job. Use this interview question as an opportunity to show your strong knowledge of the position and your communication abilities.
Question 3
Question #3: How do you stay up to date with the latest news and trends in machine learning?
How to answer
How to answer: By talking about how you're up to date with the latest news and trends in machine learning, you can show an employer that you're engaged in the industry, a skilled researcher, and self-motivated.
8,203 machine learning engineer interview questions shared by candidates
How imbalanced data causes issue in Classification ? How is it handled ? What are the evaluation metrics for such scenario ? Which one to choose and why ?
Specific experience related to the role.
How might customers want to order the search results?
Difference between Lasso and Ridge regression? When to use one over the other?
Describing my previous experience, ML theory (some of which was fundamental stuff which I struggled to recall, but most of it was straightforward and what you'd expect from an ML theory interview), and walking through a case study with the interviewer (presented with a modelling opportunity, asked what things I would need to consider and walking through the steps to get the ML model over the line).
They are very interested in that besides pure machine learning knowledge you also understand the broader business context (i.e. how and where ML can solve business problems for Deliveroo)
What's your opinion on working >40 hours a week (rather 50-60, 40 being an exception)
Difference between linear and logistic regression. Difference between lasso and ridge regression. Bagging, boosting, forms of regularisations in algorithms. What is the operation behind convolution.
What is your experience in ML?
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