First round was a non-technical but complete assessment of behavioural scenarios, including a few questions on the candidate's long-term professional ambitions, as well as inquisitions on your general knowledge on AI as well as on the company itself. Second round was a technical and knowledge-based interview on many scientific concepts, testing the candidates on both their understanding of classical ML algorithms as well as their knowledge on more recent developments. Your problem solving approach may also be tested through a live-coding challenge. Final round was a technical homework assignment to be completed in a week, and later presented to the team, followed by a Q&A to understand your approach and decisions.
Machine Learning Intern Interview Questions
8,210 machine learning intern interview questions shared by candidates
General questions about machine learning, training data split, computer vision ANNs, transfer learning, metrics, past experiences etc. Focused leetcode style coding question.
They asked a python question similar to a leetcode question.
- Pandas: data cleaning exercise - Numpy: 2d array manipulation (rotations, etc) - SQL: not easy if you haven't done those kind of exercises in sql before. Bus and passengers exercise.
What is the difference between K-Means and KNN algorithms, in which situations would they be applicable?
Code pairing round involved solving some failing test cases and implementing a machine learning based solution on a problem
yes , i have no question
What are the differences between Gaussian Mixture Model clusterization and K-means?
Questions on deep learning (CNN), machine learning (SVM, Kernel), NLP (stemming, lemmatization, word2vec, glove) plus other questions related to the research work.
tell me about yourself. Telecom background.
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