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

4.7
stars
510 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 102 Reviews for Supervised Machine Learning: Regression

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

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 Sid C

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 Gianluca P

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

Feb 24, 2022

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

By Pankaj Z

Apr 19, 2021

Very helpful course. There are few ups and downs but overall its helpful.

By Mehdi S

Jan 20, 2021

Good course with nice exemple for illustration

By Keyur U

Dec 24, 2020

A great course to kick start your ML journey.

By Bernard F

Nov 27, 2020

An truly exciting course!

By Daren L P

Feb 22, 2024

thorough and well taught

By Feri I

Aug 24, 2022

I like this is cuourse

By hassen g

Oct 20, 2022

Great course

By Iddi A A

Dec 11, 2020

Excellent

By Juhi S

May 20, 2022

GOOD

By YASH A

Apr 22, 2021

Nice

By Evangelos N

Feb 29, 2024

Overall a good course. Nothing special though. In detail: Pros: 1. Very good example code (jupyter notebooks) given. Can even be studied stanalone. Can be used as a reference for future cases. 2. Provides an holistic view in the regression pipeline. Cons: 1. The course is outdated and not very professional and this is obvious in various examples, to name a few: a) There are some syntax errors in the notebooks. b) There are English grammatical/syntax errors. c) There is content in the notebooks that was never introduced in the videos (SGD). d) There are video duplicates with different naming. e) The provided notebooks (normally 2 notebooks) each week are sometimes provided is wrong chronological order. 2. The course lacks mathematical foundation. In order to fully understand the topic you need to read theory from other resources in parallel. 3. The instructor clearly reads a pre-written text and making his speech monotonic and hard to follow. 4. The slides are boring and highly simplistic.

By Jacob J

Nov 6, 2022

The content was great. However, there were numerous typos and more than half of the time the labs either wouldn't load and/or the notebooks were not the same as the videos. This was similar as the prior course.

By Andre S

Oct 1, 2023

Added extra good content, but poor explanation. Graded quiz are not well explained in the course.

By Carlos J C

Sep 26, 2023

Too many errors in exams. Repeated videos and deprecated python codes.

By Khalid M

Mar 23, 2023

Good course , but many videos should be explained more visually

By 90303433 - L A G R

Dec 5, 2023

Algunos notebooks marcan error.

By Saman F

Feb 17, 2023

good and its very helpfull

By HARSHA V

Oct 17, 2023

ok

By Julian U C

Apr 9, 2023

It is a very bad course. I am sorry, but you are not clear enough with the theme. I have read every notebook and it is missing a lot of information.