what is the recenly project you have finished, which machine learning methods and approach you are using to achieve the goal
Machine Learning Engineer Interview Questions
Machine Learning Engineer Interview Questions
Les entreprises s’appuient sur les machine learning engineers pour les aider à concevoir et à améliorer les systèmes qui permettent à leurs logiciels de s’améliorer eux-mêmes, plutôt que d’être programmés. Au cours de l’entretien, préparez-vous à être longuement interrogé sur vos connaissances en informatique et en science des données et, en particulier, sur votre capacité à reconnaître des modèles et des tendances. Un diplôme en informatique ou dans un domaine équivalent sera exigé.
Questions d'entretien d'embauche fréquentes pour un machine learning engineer (H/F) et comment y répondre
Question 1 : Quels sont les algorithmes, termes de programmation et théories les plus importants à maîtriser en tant que machine learning engineer ?
Question 2 : Comment expliquer l’apprentissage automatique à quelqu’un qui ne comprend pas ce domaine ?
Question 3 : Comment se tenir informé des dernières nouveautés et tendances en matière d’apprentissage automatique ?
8,202 machine learning engineer interview questions shared by candidates
Amazon asks on leadership principles. This was unexpected.
How does a computer work
Don't remember exactly but it was a coding exercise involving grouping and finding intersections between items containing text fields. Wasn't too hard but I got stuck under the interview pressure.
Don't remember exactly but it was a coding exercise involving grouping and finding intersections between items containing text fields. Wasn't too hard but I got stuck under the interview pressure.
Behavioral questions were heavily oriented towards the Amazon leadership qualities. > Name a time you were innovative > Name a time you delivered a simple solution to a complex problem. Follow up questions included how to quantify the level of success in projects brought up. Machine learning fundamentals: > How to deal with a troublesome dataset (interpretation open ended so think data cleaning, etc.) > How to deal with misrepresentative training data (imbalanced dataset, overfitting, explain how L1/L2 regularization work at an optimization level) > How to deal with a large dataset where only a few examples are labeled (semi-supervised learning) Coding question was: https://leetcode.com/problems/find-original-array-from-doubled-array/
HackerRank assessment included a variation of Leetcode's Shortest Path to Get Food problem
program k-nearest neighbour from draft
Describe an example where you had multiple alternatives to choose from for tools/approaches for a project. How did you go forward?
Predicting prices.
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