L1 and L2 Regularization in Machine Learning: Easy Explanation for Data Science Interviews
Regularization is a machine learning technique that introduces a regularization term to the loss function of a model in order to improve the generalization of a model. In this video, I explain both L1 and L2 regularizations, the main differences between the two methods, and leave you with helpful pros and cons so you can best decide when to implement each function. 🟢Get all my free data science interview resources https://www.emmading.com/resources 🟡 Product Case Interview Cheatsheet https://www.emmading.com/product-case... 🟠 Statistics Interview Cheatsheet https://www.emmading.com/statistics-i... 🟣 Behavioral Interview Cheatsheet https://www.emmading.com/behavioral-i... 🔵 Data Science Resume Checklist https://www.emmading.com/data-science... ✅ 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: / emmading001 ==================== Contents of this video: ==================== 00:00 Introduction 00:21 Interview Questions 00:41 What is regularization? 01:27 When to use regularization? 01:47 Regularization techniques 03:44 L1 and L2 regularizations 03:55 L1 Regularization 08:03 L2 Regularization 10:50 L1 vs. L2 Regularization 11:47 Outro
Handling Imbalanced Dataset in Machine Learning: Easy Explanation for Data Science Interviews
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