Machine Learning Interview Questions

Machine Learning Interview Questions & Answers

Machine Learning is a newly emerging term in IT field, which is a subset of Artificial Intelligence. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. It can help to detect fraudulent transactions, online recommendation, sentiments of social media content. It can even help out to decide which is the best place to open up a restaurant, hotel etc.

This workbook contains extensive set of Machine Learning questions and answers that can help clear your understanding on the topic and also help in responding to questions posed to an interviewee. With the difficulty level of questions ranging from low to high, we intend to cater to the requirements of masses.

This guide will benefit:

A beginner who has never faced any Machine Learning / Data Science interview

Anyone who wants a brief on Machine Learning

Professional who want answers with examples and explanation

One who don't know what "They" really want to hear.

How should you read this book?

You have to first do a slow reading of all the questions in this book. Once you go through them in the first pass, mark the questions that you could not answer by yourself. Then, in second pass go through only the difficult questions. After going through this book 2-3 times, you will be well prepared to face a technical interview for a Machine Learning position.

What are the sample questions in this book?

1. List of assumptions in linear regression?

2. What is normal distribution? Why do we care about it?

3. How do we verify if a feature follows the normal distribution or not?

4. What is SGD (Stochastic Gradient Descent? How is it different from gradient descent?

5. What all metrics do we use for evaluating regression models?

6. Share some examples where a false positive is important than a false negative?

7. What is Precision-recall trade-off?

8. Can we use L1 regularization for feature selection?

9. What happens when we have correlated features in our data?

10. How we can incorporate implicit feedback (clicks etc.) into recommender systems?

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