Machine Learning with Ridge and Lasso Regression
Master regularization with ridge and lasso regression: from theoretical foundations to practical applications
Start for free
What you get:
- 1 hour of content
- 20 Interactive exercises
- 7 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
Machine Learning with Ridge and Lasso Regression
Start for free
What you get:
- 1 hour of content
- 20 Interactive exercises
- 7 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
Start for free
What you get:
- 1 hour of content
- 20 Interactive exercises
- 7 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
What You Learn
- Master ridge and lasso regression to elevate your data analysis skills to the next level
- Gain a deep understanding of ridge and lasso regularization and how they can be applied to solve real-world problems
- Understand the strengths and limitations of ridge and lasso regression and master their use to prevent overfitting
- Explore the key differences between ridge and lasso regression and learn how to choose the right method for your use case
- Integrate essential math concepts with hands-on Python programming skills
- Develop the skills to independently plan, execute, and deliver a complete ML project from start to finish
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Industry leaders and professionals globally rely on this top-rated course to enhance their skills.
Course Description
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.
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1.1 What does the course cover?
5 min
1.2 Regression Analysis Overview
3 min
1.3 Overfitting and Multicollinearity
3 min
1.5 Introduction to Regularization
3 min
1.6 Ridge Regression Basics
6 min
1.8 Ridge Regression Mechanics
6 min
Interactive Exercises
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.
Curriculum
- 1. Regularization Basics9 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?5 minRegression Analysis Overview3 minOverfitting and Multicollinearity3 minIntroduction to Regularization3 minRidge Regression Basics6 minRidge Regression Mechanics6 minRegularization in More Complicated Scenarios3 minLasso Regression Basics3 minLasso Regression vs Ridge Regression4 min - 2. Setting Up The Environment2 Lessons 4 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 Read now1 minImporting the Relevant Packages3 min - 3. 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 Preparation6 minExploratory Data Analysis6 minPerforming Linear Regression8 minCross-validation for Choosing a Tuning Parameter3 minPerforming Ridge Regression with Cross-validation5 minPerforming Lasso Regression with Cross-validation3 minComparing the Results4 minReplacing the Missing Values in the DataFrame2 min
Topics
Course Requirements
- You need to complete an introduction to Python before taking this course
- Basic skills in statistics, probability, and linear algebra are required
- It is highly recommended to take the Machine Learning in Python course first
- You will need to install the Anaconda package, which includes Jupyter Notebook
Who Should Take This Course?
Level of difficulty: Intermediate
- Aspiring data scientists and ML engineers
Exams and Certification
A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.
Meet Your Instructor
Ivan has a background in systems and sound engineering, along with information technologies and communications. In addition, he has professional experience in the media production industry and telecommunications. Ivan believes the value of data is growing every day, and it will soon be the biggest commodity in the world. He describes himself as “forward-looking and visionary”. Besides data analysis, data collection, and Python programming, he is passionate about artificial intelligence, signal processing, sound design, acoustics, and music. He sees these subjects as interconnected, and his work goal is to keep the balance between science and arts.
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