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Hello and welcome to this video on regularization and elastic net regression!

In this video, we'll be discussing regularization, which is a technique used to prevent overfitting in machine learning models. We'll also be looking at elastic net regression, which is a combination of L1 and L2 regularization techniques.

We'll start by discussing what overfitting is and why it's a problem in machine learning. We'll then introduce regularization and explain how it works to reduce overfitting by adding constraints to the model.

Next, we'll discuss L1 and L2 regularization techniques and how they differ. We'll then introduce elastic net regression, which is a combination of these two techniques.

Finally, we'll go over some examples of how to implement elastic net regression in Python using the scikit-learn library.

By the end of this video, you should have a good understanding of regularization and elastic net regression and how to implement them in your own machine learning projects.

Thank you for watching! Be sure to like and subscribe for more videos on machine learning and data science.

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