Cracking the Machine Learning Interview: Common Questions and Best Answers

In recent years, machine learning has emerged as a crucial technology for businesses across industries. From AI-powered chatbots to predicting consumer behavior, the possibilities are endless. However, with this growth in demand for machine learning experts, it has become increasingly challenging to land a machine learning role as competition has risen.

The interview process for machine learning roles is notoriously challenging. These roles require a unique set of skills and knowledge, which means hiring managers ask intricate questions during the interviewing process. Therefore, there’s a need for applicants to be adequately prepared to ace the interview.

To help you crack the machine learning interview, we’ve created a list of common questions and best answers that are likely to be asked.

Question 1: Define Machine Learning and How do you Apply it?

This question tests your fundamental understanding of machine learning. An excellent answer to this question would show a clear understanding of machine learning and the ability to apply it in real-life scenarios.

A good answer to this question could be, “Machine Learning refers to training a computer to learn and improve over time. It involves using algorithms to identify patterns and insights in large data sets. I would apply machine learning by ingesting unstructured data, cleaning, and transforming data, and then feeding it into a machine learning algorithm to predict outcomes in a business context.”

Question 2: What are the Different Types of Machine Learning?

The Different types of machine learning are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. In answering this question, you should describe each of the different types of machine learning and provide examples of when they are applied.

For example, a good answer could be, “Supervised learning is where machine learning learns based on labeled data. Unsupervised learning is where the machine identifies patterns in the data without labels. Semi-supervised learning is a combination of supervised and unsupervised where some data comes labeled, and others are not. Reinforcement learning is where a machine learns by experimenting with the environment to get the right outcome. An example of supervised learning is a chatbot that learns to identify and respond to specific phrases. Unsupervised learning may be applied in finding groups of people based on purchasing habits.”

Question 3: What is The Difference Between Overfitting and Underfitting?

This question tests the applicant’s skill in model evaluation and validation. Overfitting is when a model is too specific and only fits within a specific dataset, while underfitting is when a model is too general and cannot fit another dataset.

A good answer for this question could be, “Overfitting is when a model is trained on a set of data and becomes too specific such that it only applies to this data set. Overfitting means that a model has memorized data rather than learned from it. On the other hand, underfitting occurs when a model is too general and does not fit the data set the model is trained on, and future data it is expected to analyze.”

Question 4: Describe Your Top Three Most Used Algorithms

This question tests the applicant’s in-depth knowledge of algorithms. The top three algorithms an applicant has experience working with should be described with their advantages and disadvantages.

A good answer could be, “My top three most used algorithms are logistic regression, decision trees, and support vector machines. Logistic regression is a widely-used algorithm that can handle binary targets. Decision Trees are simple and easy to understand and are highly interpretable. Support Vector Machine is a flexible model that is useful in identifying relationships between data points.”

Undoubtedly, machine learning interviews are challenging. By preparing ahead and studying these common questions and answers, applicants can enhance their chances of landing that much-desired machine learning role.