This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
Supervised Machine Learning: Regression
Supervised Machine Learning: Regression
This course is part of multiple programs.
Instructors: Mark J Grover
55,702 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
4.7
(635 reviews)
Intermediate level
Some related experience required
Flexible schedule
Approx. 20 hours
Learn at your own pace
93%
Most learners liked this course
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
13 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
This course is available as part of
When you enroll in this course, you'll also be asked to select a specific program.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are 6 modules in this course
This module introduces a brief overview of supervised machine learning and its main applications: classification and regression. After introducing the concept of regression, you will learn its best practices, as well as how to measure error and select the regression model that best suits your data.
What's included
11 videos2 readings3 assignments2 app items
There are a few best practices to avoid overfitting of your regression models. One of these best practices is splitting your data into training and test sets. Another alternative is to use cross validation. And a third alternative is to introduce polynomial features. This module walks you through the theoretical framework and a few hands-on examples of these best practices.
What's included
7 videos1 reading3 assignments2 app items
There is a trade-off between the size of your training set and your testing set. If you use most of your data for training, you will have fewer samples to validate your model. Conversely, if you use more samples for testing, you will have fewer samples to train your model. Cross Validation will allow you to reuse your data to use more samples for training and testing.
What's included
6 videos1 reading2 assignments2 app items1 plugin
This module walks you through the theory and a few hands-on examples of regularization regressions including ridge, LASSO, and elastic net. You will realize the main pros and cons of these techniques, as well as their differences and similarities.
What's included
10 videos1 reading3 assignments1 app item
In this section, you will understand the relationship between the loss function and the different regularization types.
What's included
5 videos1 reading2 assignments2 app items
In this section you will test everything you learned
What's included
1 reading1 peer review1 app item
Instructors
Instructor ratings
4.6 (191 ratings)Offered by
Recommended if you're interested in Machine Learning
University of California San Diego
Coursera Project Network
Corporate Finance Institute
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Learner reviews
4.7
635 reviews
- 5 stars
76.68%
- 4 stars
17.21%
- 3 stars
3.91%
- 2 stars
0.78%
- 1 star
1.40%
Showing 3 of 635
GL
5
Reviewed on Jun 5, 2021
Please give the lecturer credit and include him as one of the instructors
PN
5
Reviewed on Feb 6, 2022
really good course, content is rich with good machine learning concepts
VO
5
Reviewed on Apr 9, 2021
Very well presented. This is without doubt the best series for Machine Learning on Coursera.
New to Machine Learning? Start here.
Open new doors with Coursera Plus
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy
Frequently asked questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.