what will you do when your model is overfitting or underfitting?
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.
8,212 machine learning engineer interview questions shared by candidates
Basic behavioral questions followed by a presentation explaining my assignment.
ML questions with mathematical deductions, including MLE. Technical discussions about the Kite-specific prediction tasks.
The technical phone screen was more oriented toward software engineering and pretty challenging but that made it really interesting.
Estimate multivariate gaussian population parameters from data.
Find overlapping events in a calendar
The first call - general HR questions about the company, my expertise and motivation. The technical interview - asked about my previous projects, how I approached various problems, theoretical ML related questions (e.g. what is the difference between decision tree and random forest)
In Technical interview of 30-50 minutes, -> simple basics of C like (values and referencing,memory management,stack and heap memory) ->puzzle of probability or simple geeksforgeeks puzzles(they don't see if you answer correct or not, but check your cognitive skills) ->Why the complexity of mergeSort is O(n log(n)). ->Database Questions(they check your logic not queries). ->Problem Solving Question( Again logic is important). -> Machine Learning Logic Check like Predict if maid will come tomorrow or not. Tell me which meaning attributes in data would be required to predict so. (I told 7-8 like weather, way of speaking of maid daily, her family functions and more). As you can see Interview questions were good. They are checking if you can think or not. Also other basic interview questions were asked.
Pruning of decision tree
Related with my experience. Some are from the job requirement.
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