Support Vector Machines(SVM): Must-Know Questions and Answers for Data Science Interviews

Author(s): Ajit

Originally published on Towards AI.

Support Vector Machines(SVM): Must-Know Questions and Answers for Data Science Interviews

Hey everyone! 👋
I’ve curated a focused collection of Support Vector Machine (SVM) interview questions and answers — many based on real-world data science interviews. Whether you’re preparing for a role in machine learning or looking to strengthen your understanding of SVMs, this guide is structured to help you master key concepts and boost your confidence.

This article provides a comprehensive overview of Support Vector Machines (SVM), focusing on essential interview questions and answers that cover key concepts like the maximal margin classifier, support vector classifiers, and the advantages of using kernel tricks for non-linear classification. It highlights limitations of SVMs, offers insights into decision boundaries, and details various approaches to tackle multi-class problems, ensuring thorough preparation for data science interviews.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI