A couple interview Qs I can remember are: What is the bias-variance tradeoff? What's a GBM and an example of one? What is under fitting and over fitting?
Machine Learning Developer Interview Questions
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How would you scale up a project you worked on?
Coding questions were similar to leetcode and other competitive programming
Calculate depth sum of a nested array
Two classic "leetcode" style questions, one involving a binary search and the other dynamic programming. Prepare using the classic resources available online and you will be fine.
Most questions are Leetcode medium level problems
C++ classes
https://leetcode.com/problems/merge-intervals/ https://leetcode.com/problems/validate-binary-search-tree/ I summarized my experience in the referred post above. Scheduled full loop 2months ahead to have ample time for prep (but couldn't really cover much). First codding Interview: Two questions a. Find the maximum sub-tree sum (I couldn't find related question on leetcode). Essentially consider every node in the tree as the root, them calculate the sum of the nodes, then return the maximum such sum b. Variation of https://leetcode.com/problems/word-break/. Return the output as a concatenation with the words from the dictionary separated by single space. E.g string = "catsanddogs", wordDict = ["cat", "sand","cats","dog","and"], an output = "cat sand dogs" or "cats and dogs" Second coding Interview: Two questions a. Return top k integers with highest frequencies in an array: https://leetcode.com/problems/top-k-frequent-elements/ b. Course schedule: https://leetcode.com/problems/course-schedule-ii/ Behavioural interview + one coding question a. Typical workplace behaviour questions b. Find the length of the longest sub-array whose sum is target System design: Design a Machine Learning app that makes recommendation to users for places to visit along their trip. Focus is no the ML pipeline: Data, Features, Evaluation, Model-building, Feedback, Online testing, Offline testing Personal Assessment: Didn't give good account of myself (first time system design). ML system design: Design E2E classification pipeline for Facebook marketplace. Users post visual (photos) + textual descriptions. Lots of focus on the Model training practice + Feature Engineering + Feedback loop, as well as emphasis on theory Personal Assessment: Way better than 4. Calibration (Behaviour + Coding): a. Behaviour similar to 3 b. Given a linked list L, a value val, and position pos, insert val at the posth position in L (Not replace).
Algorithms
difference between boosting and bagging
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