machine learning interview questions?

Machine learning inquiries questions are a vital piece of the information science meet and the way of turning into an information researcher, machine learning designer or information build. Springboard made a free manual for information science meets so we know precisely how they can trip competitors up! So as to help settle that, here is a curated and made a rundown of key inquiries that you could find in a machine learning meeting. There are a few responses to oblige them so you don't get puzzled. You'll have the capacity to do well in any prospective employee meet-up with machine learning inquiries in the wake of perusing this piece.
Here we found and shown below-mentioned best machine learning course interview questions so once check it out hope very useful information knowledge sharing from my side

1: What’s the trade-off between bias and variance?

2: What is the difference between supervised and unsupervised machine learning?

3: How is KNN different from k-means clustering?

4: Explain how a ROC curve works?

5: Define precision and recall?

6: What is Bayes’ Theorem? How is it useful in a machine learning context?

7: Why is “Naive” Bayes naive?

8: Explain the difference between L1 and L2 regularization.

9: What’s your favorite algorithm, and can you explain it to me in less than a minute?

10: What’s the difference between Type I and Type II error?

11: What’s a Fourier transform?

12: What’s the difference between probability and likelihood?

13: What is deep learning, and how does it contrast with other machine learning algorithms?

14: What’s the difference between a generative and discriminative model?

15: What cross-validation technique would you use on a time series dataset?

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