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Machine Learning Interview Questions

Machine Learning Interview Questions


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Hearing about an interview always makes us feel jittery. But we all know quite well that the entire process is worth suffering for as you may just end up getting your dream job. A machine learning interview is no exception. It needs a whole lot of preparation and perseverance.

You may land yourself in midst of immense confusion if you think of preparing for everything. What you need to do is focus on the prime topics that will clarify all your core concepts.

Vital Topics for Machine Learning Interview

Whether you are applying for a research role or an engineering role, you are expected to know the following topics in great detail.

Data Structures

This particular topic will be tested in more than one round of your interview. You need to have a sound knowledge of linked lists, arrays, strings, stacks, queues, etc. You can work out some of the important problems from these topics for better preparation.

Programming Language (eg. Python, R, etc)

Know about what is Python, how it works, concepts which encircle the object-oriented programming, how is ‘is’ different from ‘==’, etc.

Problem Solving Capacity

Generally, this section does not come up during the interview round. However, you should not take a chance as companies tend to have different strategies for hiring. Questions which can come from this section include something like ‘How do you model a system where you urgently want to find out fraud in a credit card?’

Engineering Roles

This role requires you to know about how models are served in production as well as the major problems behind it. Moreover, if you know about how to scale the relevant models as well as the big data pipelines, this may work as an advantage for you.

Research Roles

You should be completely aware of the mathematics and other major as well as minor concepts related to the basic machine learning. You should also be able to highlight the concepts behind the projects that you have undertaken.

Machine Learning Basics

For this, you need to be acquainted with topics like:

  • Variance Trade-Off
  • Recall Definitions
  • Regularization
  • Gradient descent

Also be clear about classification, clustering, and dimensionality reduction (Get a good idea about the various algorithms used for each topic mentioned above.)

Some Helpful Resources to Check Out Now

This is a list of some imperative resources that will provide you with in-depth learning of topics important for machine learning interview. Check them out now!

Machine Learning Interview Questions

Here is a list of some relevant questions that will help you prepare for the interview and pass it with flying colors.

Question: In case some data is corrupted or goes missing in a dataset, how will you handle it?

Answer: You can easily find the lost or corrupted data in a dataset. It depends whether one wants to drop the respective rows or columns or put a value into them as a replacement. isnull () and dropana () are two extremely important and useful methods in Pandas. Both of these will help you in finding the columns with the missing data and will enable one to drop the accurate values.

You can also use the fillna () method in case you want to replace an invalid value with a placeholder value.

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

Answer: Supervised learning requires labeled training data. You should know which data point belongs to which class or has what label. Unsupervised learning, on the other hand, does not require labeling data.

Question: What is the difference between L1 and L2 regularization?

Answer: L1 regularization is more binary — many variables are assigned a 1 or a 0 in weighting. It is like setting a Laplacian prior on the terms On the other hand, L2 regularization tends to spread the error among all the terms and corresponds to a Gaussian prior.

Question: What is the Fourier transform?

Answer: A Fourier transform is a method of decomposing the generic functions into a superposition of symmetric functions. This is similar to the way a musical chord can be expressed in terms of the volumes and frequencies (or pitches) of its constituent notes.

Question: Explain Reduced Error Pruning in a decision tree

Answer: In pruning, those branches of decision trees that have weak predictive power are removed in order to increase the predictive accuracy and reduce the complexity of the model.

In Reduced Error Pruning, we start from the leaves, and each node is replaced with its most popular class. If it doesn’t affect the predictive accuracy, the change is retained. This method of pruning is quite naive and has the advantage of speed and simplicity.

Question: What’s the F1 score? How would you use it?

Answer: F1 score is a measure of a model’s performance. It is essentially a weighted average of the recall and precision. A good F1 score means that there are low false positives and low false negatives. A perfect F1 score is 1 and the F1 score of a totally failed model is 0.

F1 score = 2*((precision * recall)/(precision + recall))

Question: What makes CNNs translation never changing?

Answer: The convolution kernel has the ability to act as its own feature detector. Let us take an example. Suppose one is doing an object detection. In this case, particularly it does not matter where the object is located in the image. This is because here one is specifically going to apply the convolution in a sliding window manner across the entire range of the image under consideration.

Question: What is the marked importance of Residual Networks?

Answer: The prime significance of Residual Networks was generally that it allowed the direct feature access from the previous layers. This directly contributes to the circulation and propagation of the information fast through the entire network. Another interesting news about this is that by the utilization of local skip connection, a multi-path structure is provided to the network. This, in turn, provides the features of different paths to propagate through the complete network.

Question: What makes segmentation CNNs have an encoder-decoder structure?

Answer: The encoder CNN can be imagined as a feature extraction network. On the other hand, the decoder CNN  can be thought of as something that uses that particular information to make out the image segments by quickly decoding the essential features and presenting and upscaling to the original size of the image.

Question: Why do classification CNNs consist of max-pooling?

Answer: This is something that has a humongous role in computer vision. Max-pooling has the ability to reduce computation due to the fact that feature maps become smaller after the pooling. One also let go off the fear that there will be too much loss of meaningful information since one is taking out the maximum activation. Max-pooling has also been given the credit of providing translation in-variance to the CNNs.

Question: What has made naive Bayes ‘naive’ in actual?

Answer: Naive Bayes has been aptly titled as ‘naive’ because it lacks the differentiation mind and quotes everything in a dataset as important and independent.

Question: A rise in the temperature of the globe has led to a decrease in the number of pirates. Does that mean that a decrease in the number of pirates has caused climate change?

Answer: A good insight into this question will make you remember the case of causation and correlation. No, we cannot definitely assume that a decrease in the number of pirates has led to a massive change in the global temperature. There may be several other factors influencing this particular outcome or phenomenon.

There could be a correlation between the total number of pirates and global average temperature but it cannot be concluded that the pirates died completely due to the increase in the global temperature.  

Question: Highlight the current master’s research that you have done. What actually worked and what did not? Can you tell about its future direction?

Answer: For this question, you can talk fluently about the connections of your research with their business. You can quickly count the skills that you learned or anything helpful that will make an impact on their business in the most appreciative fashion. Remember that your research may not be 100% connected with their business model, but it should somehow influence your role and add value to their company and your own self.

Moreover, you can explain about the ups and downs that you faced during your research and how that has influenced your direction in a positive way.

Question: Have you undertaken certain projects recently that hold relevance for our business?

Answer: This question can be answered in a similar manner just as the previous one. You can talk about your recent projects or the ones going on to leave a solid impression on the interviewer’s memory.

Important Tips That Might Help You Perform Better

Since most machine learning interviews are conducted online, you need to ensure that you have an apt Internet connection and a properly working mic. Unnecessary disturbance in between should not break the flow of your interview as this may also cause irritation to the interviewer.

  • Keep the conversation normal as this may help you appear honest.
  • You may also get some time to ask questions. For such a situation, you need to prepare good questions beforehand. Some relevant questions might be something like this:-
  • Which hardware is generally used by you?
  • Do you also allow data labeling?
  • The team consists of how many members?


We hope that by now you got a sound idea about the questions that you may encounter while attending a Machine Learning interview. The above-mentioned questions are enough to give you a thorough preparation of what is actually expected of you.

Best of luck!

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