Machine learning interview questions follow a couple of patterns. While they can seem abstract and overwhelming, we can break them down into four types of situational problems.

Modeling Case Study

The modeling case study requires a candidate to evaluate and explain a particular part of the model building process. A common case study problem would be for a candidate to explain how they would build a model for a product that exists at the company.

For example:

“Describe how you would build a model to predict Uber ETAs after a rider requests a ride.”

Many times, this can be scoped down into a specific portion of the model building process. For instance, taking the example above, we could instead reword the problem to:

• “How would you evaluate the predictions of an Uber ETA model?”
• “What features would you use to predict the Uber ETA for ride requests?”

The main point of these case questions is to determine your knowledge of the full modeling lifecycle and how you would apply it to a business scenario. In the next few chapters, we will go over each part of the modeling life-cycle.

Recommendation and Search Engines

Recommendation and search engines are questions that are technically case study questions but are asked so frequently that it’s important to conceptualize as their own category.

Example Questions

• How would you build a recommendation engine to recommend news to users on Google?
• How would you evaluate a new search engine that your co-worker built?

Machine Learning Algorithm Concepts

These types of questions exist to get an in-depth understanding of your conceptual knowledge of machine learning. Companies ask these questions mostly to machine learning and deep learning specialists that would be focusing on the specific building and training of a machine learning model.

These types of questions would be something akin to “How does random forest generate trees?” or “What’s the difference between SVM and Gradient Boosting Trees?”.

It’s clear that these questions are meant to test if candidates understand the situations in which they would apply different types of models. They’re also mostly definition based questions, so if you memorize a bunch of different machine learning definitions and applications, you will usually do okay in this part.

Applied Modeling

Applied modeling questions take machine learning concepts and ask how they could be applied to fix a certain problem. These questions are a little more nuanced, require more experience, but are great litmus tests of modeling and machine learning knowledge.

An example question would be: “You’re given a model with 90% accuracy, should you deploy it?”.

These types of questions are similar to case studies in that they are mostly ambiguous, require more contextual knowledge and information gathering from the interviewer, and are used to really test your understanding in a certain area of machine learning.

In this lesson, we’ll tackle different ways to approach these machine learning problems.