If we train a network using only one image of cat. What is the output if we flip that image?
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.
8,202 machine learning engineer interview questions shared by candidates
What is the kernel trick in SVM? How does PCA work? General questions about digital system design. What are the two kind of digital filters ? and then general questions about them, i.e why are IIR more often used, what do you have to pay attention to, etc ?
1. They give one image classification problem on classifying pictures of dogs and cats
( Onsite interview Round 2 ): White board 3sum problem, Given an array, find wether it has any subarray of size 3 with the sum equal to target value e.g. arr = [1,2,3,4,5] target = 6 Output: [1,2,3]
( Onsite interview Round 1 ): Programming: Given an array, find wether it has any contiguous subarray with the sum equal to target value e.g. intput: arr = [1,2,3,4,5] target = 7 output: [3,4]
(Coderpad Round 3 - Technical) Given a dictionary with keys and values and given an input, find closest key to the given input e.g. dictionary = { 'a' : ['b','c','e'], 'm': ['c','e'] } Input: ['c'] Output: 'm' Explaination: 'c' is at distance 1 from 'a' and 0 from 'm'. Hence closest key for 'c' is 'm'.
(Coderpad Round 3 - Technical) Given a dictionary with keys and values and given an input, find closest key to the given input e.g. dictionary = { 'a' : ['b','c','e'], 'm': ['c','e'] } Input: ['c'] Output: 'm' Explaination: 'c' is at distance 1 from 'a' and 0 from 'm'. Hence closest key for 'c' is 'm'.
How would you speed up the training of a pre-trained BERT from a public dataset to a private dataset?
Write a program to retrieve log data in an optimal way.
What do you mean by perturbation?
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