I learned a lot about Ridge and Lasso in this course. The instructor was very knowledgeable and explained the material very clearly. Thanks.
Demonstrating how you can use ridge and lasso regression to apply regularization in machine learning. This course will improve your understanding of regression analysis so you can take your data scientist abilities to the next level.
Ridge and lasso regressions are machine learning algorithms with an integrated regularization functionality. Built upon the essentials of linear regression with an additional penalty term, they serve as a calibrating tool for preventing overfitting. In this hands-on course, you will learn how to apply ridge and lasso regression in Python and determine which of the two is the best choice for your particular dataset.
This course gives you an insight into the machine learning regularization procedures and explains how these can be applied in Python.
As an introduction to the course, we explore the concept of regularization and explain how it can be leveraged to prevent overfitting and multicollinearity issues. In addition, we demonstrate the theoretical differences between the mechanisms of ridge and lasso regression.
If you’re new to programming with Python, we recommend going through our Introduction to Jupyter course which details installing Anaconda and Jupyter and features a tour of the Jupyter Environment. Here, we talk about the required packages for applying ridge and lasso regression in Python.
In this section, we will walk you through the implementation of ridge and lasso regression using sk-learn in Python. We apply these methods to a real dataset in order to increase the performance of a regression algorithm by preventing overfitting. Furthermore, we demonstrate how regularization works and uncover the differences between ridge and lasso models.
“Regularization is a necessity when dealing with issues such as overfitting and multicollinearity. In this course, I will show you how to apply this technique with the help of two machine learning algorithms that are essential for your data science journey – ridge and lasso regression.”
Content Creator at 365 Data Science
Machine Learning with Ridge and Lasso Regression
with Ivan Manov