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Muhammad

بِسمِ اللَّهِ الرَّحمٰنِ الرَّحيمِ

From: Sahiwal Division

Registered: 2012-03-22

Posts: 22,197

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65 Machine Learning Engineer Interview Questions And Answers

Engineering :: Machine Learning Engineer Job Interview Questions and Answers

Machine Learning Engineer related Frequently Asked Questions by expert members with job experience as Machine Learning Engineer. These questions and answers will help you strengthen your technical skills, prepare for the new job interview and quickly revise your concepts

1 Tell us what is the difference between supervised and unsupervised machine learning?
2 Tell us when will you use classification over regression?
3 Can you name some feature extraction techniques used for dimensionality reduction?
4 Tell me how does deep learning contrast with other machine learning algorithms?
5 Tell us how do bias and variance play out in machine learning?
6 Tell me what are your favorite use cases of machine learning models?
7 Tell us which data visualization libraries do you use? What are your thoughts on the best data visualization tools?
8 How would you evaluate a logistic regression model?
9 Tell us what’s the difference between a generative and discriminative model?
10 Tell me how a ROC curve works?
11 Tell me what is the most frequent metric to assess model accuracy for classification problems?
12 Explain me machine learning in to a layperson?
13 Can you explain what is the difference between inductive machine learning and deductive machine learning?
14 Tell us how do classification and regression differ?
15 Explain me what is machine learning?
16 Tell me what are the last machine learning papers you’ve read?
17 Explain me a hash table?
18 Tell us what evaluation approaches would you work to gauge the effectiveness of a machine learning model?
19 Explain me how would you handle an imbalanced dataset?
20 Tell us how is a decision tree pruned?
21 Explain me what’s the trade-off between bias and variance?
22 Explain me what cross-validation technique would you use on a time series dataset?
23 Tell us which one would you prefer to choose – model accuracy or model performance?
24 Tell us how will you know which machine learning algorithm to choose for your classification problem?
25 Please explain what is deep learning?
26 Do you know what’s the “kernel trick” and how is it useful?
27 Please explain what is deep learning, and how does it contrast with other machine learning algorithms?
28 Explain me what is Bayes’ Theorem? How is it useful in a machine learning context?
29 Do you know which is more important to you– model accuracy, or model performance?
30 Tell us how do you ensure you’re not overfitting with a model?
31 Tell us what do you think of our current data process?
32 Tell us what is your training in machine learning and what types of hands-on experience do you have?
33 Tell us why is Naïve Bayes machine learning algorithm naïve?
34 Tell me how is KNN different from k-means clustering?
35 Explain me what’s your favorite algorithm, and can you explain it to me in less than a minute?
36 Can you list some use cases where classification machine learning algorithms can be used?
37 Tell me what is precision and recall?
38 Tell us an example where ensemble techniques might be useful?
39 Tell us how can we use your machine learning skills to generate revenue?
40 Tell me what is supervised versus unsupervised learning?
41 Tell me how much data will you allocate for your training, validation and test sets?
42 Tell us why is “Naive” Bayes naive?
43 Tell me do you have experience with Spark or big data tools for machine learning?
44 Tell us what is decision tree classification?
45 Tell us what kind of problems does regularization solve?
46 Tell us how do deductive and inductive machine learning differ?
47 Tell us when should you use classification over regression?
48 Tell us what’s the difference between Type I and Type II error?
49 Explain how do you think Google is training data for self-driving cars?
50 Tell us what are some differences between a linked list and an array?
51 Tell us what’s a Fourier transform?
52 Tell us how would you approach the “Netflix Prize” competition?
53 Tell us which do you think is more important: model accuracy or model performance?
54 Tell us do you have research experience in machine learning?
55 Tell us how do you handle missing or corrupted data in a dataset?
56 Tell me what is the difference between bias and variance?
57 Can you pick an algorithm. Write the psuedo-code for a parallel implementation?
58 What is the difference between L1 and L2 regularization?
59 Tell us where do you usually source datasets?
60 Tell me what is a recommendation system?
61 Explain what are some methods of reducing dimensionality?
62 Tell us what’s the F1 score? How would you use it?
63 Tell me how do you choose an algorithm for a classification problem?
64 Tell me how would you implement a recommendation system for our company’s users?
65 Do you know what is kernel SVM?

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