Explain feature selection
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 many years do you have of experience?
They used a online tool with python and R IDE. Question were on classification, CNN, linear modelling.
What were you doing in your previous role?
DEEP LEARNING, NLP, ML, Python, Pandas
Following is a coding question: coding for the decision tree from scratch. The interview will give some hints along the way. But still need to understand the algorithm itself.
What is the difference between an iterator and a generator What is the difference between a list and a set. What is the time complexity to search an item in each? What is the difference between multi-threading and multi-processing? when do you use each? What is the difference between is and == operator? What is the difference between Where clause and have clause in SQL? What is a loss function? What is logistic regression? Can you write down the logit function? What loss functions we use for regression? What is the difference between decision trees, random forest, and gradient boosting? What is the difference between L1 and L2 regularisation? Why we need to standardise features? How to explain models? Have you used the shap method before to explain a model? How do we know features importance in linear regression? what might affect this? What is negative sampling?
What is a time you had a conflict in your workplace that you had to resolve
What ML method would you apply to solve this problem? Why would that method be a better choice than other model types?
Motivation to join tidal and the technical assignment
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