16.11.2022
I highly recommend this course to my friends .
Its very comprensive and easy . thank you so much
Machine Learning with Ridge and Lasso Regression
with
Ivan Manov
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.
1 hour 19 lessons
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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 Hour of Video
Certificate of Achievement
Topics covered
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
Curriculum
- Regularization Basics Free9 Lessons 36 Min
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.
What does the course cover? Free Regression Analysis Overview Free Overfitting and Multicollinearity Free Introduction to Regularization Free Ridge Regression Basics Free Ridge Regression Mechanics Free Regularization in More Complicated Scenarios Free Lasso Regression Basics Free Lasso Regression vs Ridge Regression Free - Setting Up The Environment2 Lessons 3 Min
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.
Setting Up The Environment Importing the Relevant Packages - Ridge and Lasso Regression – Practical Case8 Lessons 37 Min
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.
The Hitters Dataset: Preprocessing and Preparation Exploratory Data Analysis Performing Linear Regression Cross-validation for Choosing a Tuning Parameter Performing Ridge Regression with Cross-validation Performing Lasso Regression with Cross-validation Comparing the Results Replacing the Missing Values in the DataFrame
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“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.”
Ivan Manov
Content Creator at 365 Data Science
Machine Learning with Ridge and Lasso Regression
with Ivan Manov