For some, landing a dream job can be a cakewalk, while for others doubling their efforts is the only way to success. If you are someone who wishes to make a successful career in Machine Learning, preparing for the interview round should definitely be on your checklist. Mainly because hiring managers ask an array of technical and behavioural questions to test your capabilities. And the only way to answer their questions brilliantly is to prepare for them beforehand.
This article lists some of the top interview questions frequently asked by interviewers, to help you prepare.
What Are The Top Machine Learning Interview Questions?
Machine learning is a branch of artificial intelligence that uses data and algorithms to imitate the human brain and perform activities similar to it without interference. A machine learning professional’s primary responsibility is to design machine learning systems and self-running artificial intelligence (AI) software to automate predictive models.
When preparing for an interview for the machine learning or data science professional role, it is imperative to prepare beforehand. Below are the top machine learning interview questions frequently asked by hiring managers.
- What is AI, and how is it different from machine learning?
- What is the difference between Type I and Type II errors?
- Which machine learning algorithm do you frequently use and why?
- What machine learning certifications do you have?
- How do you deal with instances of missing or corrupted data?
- How do you evaluate the effectiveness of the machine learning model you create?
- Give us an example of a parametric model.
- What is the meaning of precision and recall?
- Explain the terms bias and variance.
- What is meant by the term ‘overfitting’? How can it be avoided?
These were some of the general machine-learning interview questions. Questions can vary based on if you are a fresher or a professional. Let’s take a look at some of the niche questions.
What Is The Common Machine Learning Interview Question And Their Answers Asked By Interviewers To Freshers?
- How are deep learning and machine learning different?
A machine learning professional must have a basic understanding of data science , machine learning, deep learning, and artificial intelligence, among others. Therefore, this makes for a vital machine learning interview question for freshers, which you can answer like this:
Machine learning is a branch of artificial intelligence that helps discover patterns from data sets. Meanwhile, deep learning is a subset of machine learning, a neural network that imitates human behaviour.
- Why is Bayes referred to as ‘Naïve Bayes’?
Naïve Bayes is a machine learning algorithm used to solve business problems. It is referred to as naïve because it assumes that it is hard to find real-life data with all features like crucial, independent, and equal. Interviewers ask this question to evaluate the aspirant’s technical soundness. Therefore, you should be well-versed with different machine learning algorithms before going to the interview, and it can increase your chances of landing the dream job.
- What is ensemble learning?
As a new machine learning professional, it is crucial that you know different concepts related to the field and be well-versed in its practical application. Meanwhile, ensemble learning is a combination of learning algorithms used to optimize the predictive performance of a model. In this method, many algorithms are combined and strategically used to prevent overfitting, another common phenomenon.
In the next part of the article, we will list three common questions often asked by the interviewer to working professionals.
What Are The Common Machine Learning Interview Questions And Their Answers That Interviewers Ask Working Professionals?
- How to handle missing or corrupted data in a dataset?
A machine learning professional must be well-versed in how to find the missing or corrupted data in a dataset. Different algorithm libraries offer other solutions to missing or corrupted data. However, Pandas library has two great methods for dealing with missing or corrupted data: isnull() and dropna(). Therefore, knowing the answer to these interview questions helps the capabilities and knowledge of the candidate.
- Why is the decision tree pruned?
A decision tree is a flowchart showing a clear pathway to a business decision. As a machine learning professional, you will be dealing with different types of algorithms and decision trees is one of them. While working with decision trees, it is essential to know that they must be regularly pruned to eliminate the branches’ weak predictive abilities. Pruning helps minimize risks and increases the effectiveness of the decision tree model.
- What is the difference between Type 1 and Type 2 errors?
Although the chance of error reduces with increasing degrees of automation, whenever one thinks of error in machine learning algorithms, it is Type 1 and Type 2. While Type 1 error is a false positive error that claims that an event has occurred, but nothing has happened. Type 2 error is a false negative error that believes nothing has occurred, but there has been a discrepancy.
Knowing this is imperative as there are high chances of interviewers asking about it to test your knowledge about predictive models and machine learning algorithms.
Preparing for the interview questions mentioned above will help you land your dream job. Moreover, if you want to gain a competitive advantage, you can take additional courses to help refresh your knowledge about data analytics and science, among others. Emeritus India has partnered with Indian universities to offer some of the best data science courses for freshers as well as working professionals. These data science certification courses are offered in different formats like video lessons, peer-to-peer learning, assignments, practical case studies, capstone projects, etc.