ML interviews are more practical than they were a couple of years ago. You still need to know the classic topics, bias-variance tradeoff, regularization, cross-validation, evaluation metrics. But many interview loops now spend more time on applied questions: how you would build a model for a real product, what features you would choose, how you would evaluate it after launch, and what you would do when offline metrics do not match production behavior. This article is adapted from PracHub's Machine Learning Interview Questions: Complete 2026 Guide , which is based on a large set of ML interview questions collected by company and role. What ML interviews actually cover Based on 583 ML questions on PracHub, the distribution looks roughly like this: Fundamentals, 30-40% This is still the largest bucket. If your basics are shaky, it shows fast.…