Machine Learning with Ridge and Lasso Regression

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.

with Ivan Manov

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Course Overview

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.

19 High Quality Lessons
4 Practical Tasks
1 Hours of Video
Certificate of Achievement

Skills you will gain

machine learningpythontheory

What You'll Learn

This course gives you an insight into the machine learning regularization procedures and explains how these can be applied in Python.

Understand the principle of regularization
Learn about the differences between ridge and lasso regressions
Tune regression analysis algorithms to improve their performance
Handle overfitting and multicollinearity issues
Choose the proper tuning parameters with cross-validation

“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.”

Machine Learning with Ridge and Lasso Regression

with Ivan Manov

Start Course