M K Junayed P.
Before 365:
Product development | Bikroy
After 365:
Assistant Manager (Analyst) | Green Delta Insurance PLC
See all reviews
Online Course
free
Machine Learning with Ridge and Lasso Regression
with
Ivan Manov
Master regularization with ridge and lasso regression: from theoretical foundations to practical applications
2875 students already have enrolled
Skill level:
Intermediate
Duration:
1 hour
-
Lessons (1 hour)
-
Practice exams (15 minutes)
- Lessons (1 hour)
- Practice exams (15 minutes)
CPE credits:
2.5
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited:
certificate
What You Learn
- Master ridge and lasso regression for data analysis.
- Understand ridge and lasso regularization in real-world problems.
- Learn their strengths, limitations, and role in preventing overfitting.
- Explore the differences between ridge and lasso to choose the right one.
- Integrate math concepts with hands-on Python programming.
Topics & tools
theorypythonridge regressionlasso regressionregularizationmachine learningcross validationmachine and deep learning
Your instructor
AI & Data Science Course Creator | Sound Engineer
Track record
Bringing real-world expertise from leading global companies
Academic background
Master's degree, Sound Engineering
Media and recognition
- Author of Speech Recognition with Python on 365DS & Udemy
- Speaker at Power Platform Bootcamp 2025 on Audio AI
- Sound Engineer for TV/Film incl. The Bachelor & award-winners
Course OVERVIEW
Description
CPE Credits: 2.5
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
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.
Prerequisites
- Python (version 3.8 or later), Streamlit library, OpenAI API key, and a code editor or IDE (e.g., VS Code or Jupyter Notebook)
- Intermediate Python skills are required.
- Familiarity with basic statistics and linear algebra is helpful but not mandatory.
Advanced preparation
Curriculum
19 lessons
20 exercises
2 exams
- 2. Setting Up The Environment4 MinIf 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.
- 3. Ridge and Lasso Regression – Practical Case37 MinIn 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 FreeExploratory Data Analysis FreeExercise FreePerforming Linear Regression FreeCross-validation for Choosing a Tuning Parameter FreeExercise FreePerforming Ridge Regression with Cross-validation FreePerforming Lasso Regression with Cross-validation FreeExercise FreeComparing the Results FreeReplacing the Missing Values in the DataFrame FreeExercise FreePractice exam Free
- 4. Course exam25 Min
Free preview
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
96%
of our students recommend
365 Data Science.
$29,000
average salary increase
after moving to an AI and data science career
94%
of AI and data science graduates
successfully change
or advance their careers.
ACCREDITED certificates
Craft a resume and LinkedIn profile you’re proud of—featuring certificates recognized by leading global
institutions.
Earn CPE-accredited credentials that showcase your dedication, growth, and essential skills—the qualities
employers value most.
How it WORKS
Student REVIEWS
