Machine learning related questions always take a large portion during interviews. Positions like data scientists, machine learning engineers require potential candidates to have comprehensive understandings of machine learning models and be familiar with conducting analysis using these models. While discussing some of your projects with the interviewer demonstrate your understanding of certain models, it is expected that interviewers will ask some fundamental machine learning questions about model selection, feature selection, feature engineering, model evaluation, etc. In this article, I will go over 20 machine learning related questions and explain how would I answer these questions during interviews.

Model Specifics

1, What is supervised machine learning problems, and what is unsupervised machine learning problems?

You can easily distinguish them by checking whether there are target values, or labels, to predict in the problem. Supervised machine learning maps data with target values so that the model will use features extracted from data to predict target values. For example, using linear regression to predict the housing prices; using logistic regression to predict whether one person will default on his/her debts.

Unsupervised machine learning problems have no target value to predict but are learning to uncover the general patterns from the data. For example, clustering the data based on the pattern; dimension reduction based on the feature variances.

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