About the questions

The questions in this book were selected out of thousands of questions, most have been asked in actual interviews for machine learning roles. You will find several questions that are technically incorrect or ambiguous. This is on purpose. Sometimes, interviewers ask these questions to see whether candidates will correct them, point out the edge cases, or ask for clarification. For these questions, the accompanying hints should help clarify the ambiguity or technical incorrectness.

Machine learning is a tool, and to effectively use any tool, we should know how, why, or when to use it on top of knowing what it is. Because the “what” questions can be easily found online, and if something can be easily acquired, it isn’t worth testing for. This book focuses on the “how”, “why”, and “when” questions. For example, instead of asking for the exact algorithm for K-means clustering, the question asks in what scenarios K-means doesn’t work. You don’t need to understand K-means to cite its definition, but you do to know when not to use it.

Still, this book contains a small number of “what” questions. While they aren’t good interview questions, they are good for interview preparation.