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Learner Reviews & Feedback for Supervised Machine Learning: Regression by IBM

4.7
stars
620 ratings

About the Course

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. By the end of this course you should be able to: Differentiate uses and applications of classification and regression in the context of supervised machine learning  Describe and use linear regression models Use a variety of error metrics to compare and select a linear regression model that best suits your data Articulate why regularization may help prevent overfitting Use regularization regressions: Ridge, LASSO, and Elastic net   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience  with Supervised Machine Learning Regression techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics....

Top reviews

MM

Sep 21, 2022

This course is very helpful. The wonderfull part in this course was the final course project in which I had to create my own linear regression model by adding polynimial features and regularization.

GP

Nov 23, 2022

Great Course curated by IBM team. It is really designed well and helps to achieve the goal. It is as per the industry standard, and practical. One can do this course thoroughly and get a job.

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76 - 100 of 121 Reviews for Supervised Machine Learning: Regression

By Rorisang S

•

May 4, 2021

Excellent!

By Janier R

•

Mar 4, 2024

thank you

By Varun S

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Dec 25, 2022

Excellent

By Takahide M

•

Jul 13, 2022

amazing.

By Abdur R K

•

Sep 16, 2021

excellent

By Hariom K

•

Jan 23, 2022

Thanks

By Tahani L

•

Nov 8, 2024

جميلة

By Guru P N

•

Sep 24, 2022

Good

By Saeid S S

•

Apr 13, 2022

great

By Volodymyr

•

Jul 15, 2021

Super

By shashank s

•

Sep 8, 2024

good

By That L Q

•

Jun 27, 2024

Good

By Chunduri S N V S M

•

Jul 21, 2022

good

By Harshita B

•

Mar 29, 2022

Good

By Rohit p

•

Oct 18, 2021

best

By MUPPIDI H

•

Aug 16, 2022

ok

By Dr. R M

•

Jun 2, 2024

-

By Dan M

•

Feb 13, 2023

As someone with a science background, I have done a great deal of curve/model fitting. This course seems like it would be a useful introduction to these areas. As with other courses in this series, this course displays some useful shortcuts and streamlined methods for doing this work and the coded examples are useful to keep as go-bys for use in future work. On the downside, this course only covers variations on fitting a straight line to your data so it feels rather basic to be classed as "machine learning", and is simpler than I would have hoped for an intermediate course.

By Nawab K

•

Sep 12, 2023

this course was awesome from learning point of view as it was more detailed and required pre beginners knowledge about key concepts to move ahead . i have learned many concepts about machine learning models,

statistics , theory implementation part.

what i most enjoyed was the lab work as it was more detailed and there were plenty of things to learn from .

By Hossam G M

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Jun 22, 2021

This course is very great. it focuses mainly on codes and how to get your models trained well with the best results. and for that a prior knowledge of the algorithms and the coding language in addition to the different libraries would be better.

By Sebastian W

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Jun 20, 2024

Easy to understand and apply (+). Some code uses deprecated functions/methods. (-) Assignment answers seem to be mixed up (on very few occasions) so one has to randomly try out the correct answer to get 100%. (-) Issues reported.

By Sid C

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Mar 21, 2022

4/5 simply because not all the lesson Jupyter Notebooks are downloadable--the download links do not work. But the course content is very educational and has a good balance of difficulty enough to challenge you while learning.

By Abdulwaliyi J

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Aug 18, 2024

It's a nice course it deserve a 5/5 but some common and better regression algorithm like Decision Trees and Random Forest were not taught unlike the Classification part. Thanks

By Gianluca P

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Jun 4, 2021

very clear contents and explanations. Regression methods are thoroughly explained. Examples of coding are indeed a very good basis to start coding on the project.

By Gourav G

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Feb 24, 2022

AN amazing course and contain really time values content only regret is that coursera doesn't come in dark mode