Machine Learning Introduction With Regression
Build and evaluate basic regression models for predictions.
Machine Learning Introduction With Regression is a compact, hands-on Codecademy course that introduces supervised machine learning through simple and multiple linear regression.
It emphasizes practical skills—building regression models, making predictions, and evaluating model accuracy—using guided exercises and a real practice project to reinforce concepts.
Targeted at beginners and data-minded professionals, the course provides a practical entry point into predictive modeling and model evaluation and pairs short lessons with AI-assisted guidance to speed up learning and debugging.
Completing the course should leave learners able to implement basic regression workflows and interpret results, providing a foundation for further applied machine learning study or portfolio development.
At a Glance
Machine Learning: Introduction with Regression is a beginner-focused course offered by Codecademy and taught by the platform’s instructional team, designed to introduce practical machine learning concepts through guided exercises.
It broadly covers simple and multiple linear regression, model evaluation, and hands-on application via a guided practice project.
| Level | Beginner |
| Rating | 4.6 out of 5 |
| Duration | 3+ hours |
| Languages | English |
| Learners | 10K+ learners |
| Certificate | Certificate of completion (available with Plus or Pro) |
| Access | Free course content available on the platform; full features and certificate require a paid subscription |
| Course includes |
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| Price | Free to access; certificate and additional features require a paid subscription |
What This Course Teaches
This course frames outcomes as measurable competencies that prepare learners to implement basic supervised learning workflows using regression.
Students will build regression models, apply them to make predictions, and evaluate their accuracy using standard metrics and diagnostics.
How the Course Is Structured
The course is organized as a short, focused sequence of three lessons that progress from core concepts to practical regression techniques.
Overall completion time for the full sequence is approximately 3 hours.
Curriculum overview
01 Introduction to Machine Learning ▾
Covers what machine learning is and how supervised learning is applied in practical contexts.
02 Linear Regression ▾
Explains fitting a line to a set of points and using simple linear regression to make predictions.
03 Multiple Linear Regression ▾
Introduces regression with two or more independent variables to predict a dependent variable.
Audience & Requirements
This beginner-oriented course is intended for learners who want a practical, hands-on introduction to supervised machine learning using regression techniques.
It targets students, data enthusiasts, and professionals seeking a compact, applied introduction to building and evaluating simple and multiple linear regression models.
- Beginners new to machine learning and supervised learning.
- Students or researchers who need foundational regression skills.
- Data enthusiasts and analysts who want to build simple predictive models.
- Professionals seeking a quick upskill in applied predictive modeling.
- No special prerequisites.
Final Verdict
Given its strong user rating and sizable learner base, this course is a practical, low-risk way to gain hands-on exposure to simple and multiple linear regression.
Core lessons and the AI learning assistant provide an efficient, applied introduction for beginners and analysts, while the certificate and expanded platform features require a paid subscription; learners seeking job-ready depth should treat this course as a foundation and plan follow-up study or portfolio projects.
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