Principal Component Analysis (PCA) in Machine Learning: Easy Explanation for Data Science Interviews

Questions about Principal Component Analysis commonly appear in data science interviews. In this video, I’ll explain what principal component analysis is, how it works, the problems you would use PCA for, and the pros and cons associated with PCA. 🟢Get all my free data science interview resources https://www.emmading.com/resources 🟡 Product Case Interview Cheatsheet https://www.emmading.com/product-case-cheat-sheet 🟠 Statistics Interview Cheatsheet https://www.emmading.com/statistics-interview-cheat-sheet 🟣 Behavioral Interview Cheatsheet https://www.emmading.com/behavioral-interview-cheat-sheet 🔵 Data Science Resume Checklist https://www.emmading.com/data-science-resume-checklist ✅ We work with Experienced Data Scientists to help them land their next dream jobs. Apply now: https://www.emmading.com/coaching // Comment Got any questions? Something to add? Write a comment below to chat. // Let's connect on LinkedIn: https://www.linkedin.com/in/emmading001/ ==================== Contents of this video: ==================== 00:00 Introduction 00:35 What is PCA? 05:25 Steps in PCA 09:30 Pros and Cons