Top 100 Machine Learning Interview Questions and Answers

Top 100 Machine Learning Interview Questions and Answers

Here I’m giving Most Important Technical Interview Questions for Machine Learning. 

Both freshers and experienced can refer these questions to crack the interview. It will definitely help you to boost your confidence.
   

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1. What is machine learning?

In answering this question, try to show you understand the broad applications of machine learning, as well as how it fits into AI. Put it into your own words, but convey your understanding that machine learning is a form of AI that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming

2. What is candidate Sampling in Machine Learning?

A training-time optimization in which a probability is calculated for all the positive labels, using, for example, softmax, but only for a random sample of negative labels. For example, if we have an example labeled beagle and dog candidate sampling computes the predicted probabilities and corresponding loss terms for the beagle and dog class outputs in addition to a random subset of the remaining classes (cat, lollipop, fence).

 3. Mention the difference between Data Mining and Machine learning?

Machine learning relates to the study, design, and development of the algorithms that give computers the capability to learn without being explicitly programmed. While data mining can be defined as the process by which the unstructured data tries to extract hms are used.

4. What is A/Bin Machine Learning?

A statistical way of comparing two (or more) techniques, typically an incumbent against a new rival. A/B testing aims to determine not only which technique performs better but also to understand whether the difference is statistically significant. A/B testing usually considers only two techniques using one measurement, but it can be applied to any finite number of techniques and measures.

5. Explain How We Can Capture The Correlation Between Continuous And Categorical Variable?

Yes, it is possible by using the ANCOVA technique. It stands for Analysis of Covariance.
It is used to calculate the association between continuous and categorical variables.

6. How does deductive and inductive machine learning differ?

Deductive machine learning starts with a conclusion, then learns by deducing what is right or wrong about that conclusion. Inductive machine learning starts with examples from which to draw conclusions.

7. What is inductive machine learning?

The inductive machine learning involves the process of learning by examples, where a system, from a set of observed instances, tries to induce a general rule.

8. What Is The Difference Between An Array And Linked List?

An array is an ordered fashion of collection of objects. A linked list is a series of objects that are processed in sequential order.

9. Explain The  Concept Of Machine Learning And Assume That You Are Explaining This To A 5-year-old Baby?

 Yes, Machine learning is exactly the same way how day to day activities, the way they walk or sleep, etc. It is a common fact that babies cannot walk straight away and they fall and then they get up again and then try. This is the same thing when it comes to machine learning, it is all about how the algorithm is working and at the same time redefining every time to make sure the end result is as perfect as possible.

 10. What is a sigmoid function in Machine learning?

A function that maps logistic or multinomial regression output (log odds) to probabilities, returning a value between 0 and 1

11. What is bucketing in machine learning?

Converting a (usually continuous) feature into multiple binary features called buckets or bins, typically based on value range. For example, instead of representing temperature as a single continuous floating-point feature, you could chop ranges of temperatures into discrete bins. Given temperature data sensitive to a tenth of a degree, all temperatures between 0.0 and 15.0 degrees could be put into one bin, 15.1 to 30.0 degrees could be the second bin, and 30.1 to 50.0 degrees could be the third bin.

12. What are some methods of reducing dimensionality?

You can reduce dimensionality by combining features with feature engineering, removing collinear features, or using algorithmic dimensionality reduction.

13. What Is The Difference Between Machine Learning And Data Mining?

Data mining is about working on unstructured data and then extract it to a level where the interesting and unknown patterns are identified.
Machine learning is a process or a study whether it closely relates to design, development of the algorithms that provide an ability to the machines to capacity to learn.

14. What is collaborative filtering in machine learning?

Making predictions about the interests of one user based on the interests of many other users. Collaborative filtering is often used in recommendation systems.

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

This type of question tests your understanding of how to communicate complex and technical nuances with poise and the ability to summarize quickly and efficiently. Make sure you have a choice and make sure you can explain different algorithms so simply and effectively that a five-year-old could grasp the basics!

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