Interview Questions

Machine Learning Specialist Interview Questions

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Machine Learning Specialist is a professional specialized in developing Machine learning, a branch of computer science that focuses on developing algorithms which can “learn” from or adapt to the data and make predictions.

Whether you're a job seeker preparing to be interviewed for the role of Machine Learning Specialist or an employer preparing to interview candidates for Machine Learning Specialist position, these Machine Learning Specialist interview questions will help you prepare yourself for the job interview session.

Machine Learning Specialist Interview Questions

Below are a list of some skill-based Machine Learning Specialist interview questions.

1. What is machine learning?
2. Are you familiar with the term “algorithm”?
3. What are the different types of machine learning?
4. How would you explain the concept of “neural networks” to someone with no technical background?
5. What is the difference between supervised and unsupervised machine learning?
6. Provide an example of a time where you used unsupervised machine learning to solve a problem.
7. If you had to choose one type of machine learning to specialize in, which would it be and why?
8. What would you say are the most important skills for a machine learning specialist to have?
9. How well do you understand the concept of “gradient descent”?
10. When would you use a support vector machine over a neural network?
11. We want to use machine learning to improve our customer onboarding process. What types of algorithms would you suggest we use?
12. Describe your process for debugging a machine learning algorithm.
13. Which programming languages do you have experience using?
14. What do you think is the biggest challenge facing machine learning today?
15. How often do you update your skills and knowledge on machine learning?
16. Can you explain what cross-validation means in the context of machine learning?
17. What are some ways to improve generalization performance in machine learning algorithms?
18. When would you use Bayesian inference?
19. What’s the difference between model evaluation, model selection and algorithmic selection?
20. What is the maximum number of decision trees that can be used to build a random forest model? Why?
21. What do you understand about ensemble methods?
22. Can you explain what backpropagation is?
23. In deep learning, why should we avoid using the sigmoid activation function?
24. Can you give me an example of where gradient boosting has been used successfully?
25. What is the curse of dimensionality? Why does it occur and how can it be addressed?
26. What is regularization?
27. Which type of error rate is more important in evaluating the effectiveness of a machine learning model – false positives or false negatives?
28. What are some applications of regression analysis in data science?
29. What are some applications of clustering in data science?
30. What are the best practices when creating training datasets for machine learning models?
31. What’s the trade-off between bias and variance?
32. How is KNN different from k-means clustering?
33. Explain how a ROC curve works.
34. What is Bayes’ Theorem? How is it useful in a machine learning context?
35. Explain the difference between L1 and L2 regularization
36. What’s your favorite algorithm, and can you explain it to me in less than a minute?
37. What’s a Fourier transform?
38. What’s the difference between a generative and discriminative model?
39. What is deep learning, and how does it contrast with other machine learning algorithms?
40. What’s the difference between probability and likelihood?
41. What evaluation approaches would you work to gauge the effectiveness of a machine learning model?
42. Do you have experience with Spark or big data tools for machine learning?
43. How would you build a data pipeline?
44. What are the data types supported by JSON? 
45. How can we use your machine learning skills to generate revenue?
46. How would you implement a recommendation system for our company’s users?
47. How do you think Google is training data for self-driving cars?
48. What are some of your favorite APIs to explore? 
49. How do you think quantum computing will affect machine learning?
50. What is ‘training Set’ and ‘test Set’ in a Machine Learning Model? How Much Data Will You Allocate for Your Training, Validation, and Test Sets?
51. What are overfitting and underfitting?
52. What is hyperparameter optimization?
53. Explain how the Principal Components Analysis (PCA) works.
54. Give me an example of how you’ve used your data analysis to change behavior. What was the impact, and what would you do differently in retrospect?
55. Give an example of a problem you solved (or tried to solve) with machine learning.
56. Tell me about a time when you had to think outside the box to complete a task. Were you successful?
57. Can you describe a time when you had to develop a complex algorithm?
58. Can you tell me about a major success you had with a machine learning project?

Machine Learning Specialist Interview Questions and Answers

Every interview is different and the questions may vary. However, there are lots of general questions that get asked at every interview.

Below are some common questions you'd expect during Machine Learning Specialist interviews. Click on each question to see how to answer them.

1. What Is Your Greatest Accomplishment?
2. Why Should We Hire You?
3. Do You Have Any Questions for Us?
4. What is Your Greatest Strength?
5. Are You a Leader or a Follower?
6. What is Your Greatest Weakness?
7. What is Your Salary Expectation?
8. Tell Me About Yourself
9. Why Do You Want To Leave Your Current Job?
10. Why Do You Want This Job?

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