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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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101 - 121 of 121 Reviews for Supervised Machine Learning: Regression

By Pankaj Z

•

Apr 19, 2021

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

By Mehdi S

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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 Nidhi K

•

Nov 14, 2024

best course

By Iddi A A

•

Dec 11, 2020

Excellent

By R U F U S

•

Oct 6, 2024

good one

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 Patrick H

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Oct 1, 2024

The focus on the different views on regularization and their importance in the quiz seems overrated. While they are a good way to understand what regularization really is, it seems not too relevant for daily practical use. And since the different views all describe the same thing it's not a good way to have all those questions on them in the quiz, because essentially every answer would be true. Instead it would make sense to focus more on the different types of regularization, how they differ and their respective implementations in sklearn.

By Jacob J

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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

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Oct 1, 2023

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

By Carlos J

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Sep 26, 2023

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

By Khalid M

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Mar 23, 2023

Good course , but many videos should be explained more visually

By 90303433 - L A G R

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Dec 5, 2023

Algunos notebooks marcan error.

By Saman F

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Feb 17, 2023

good and its very helpfull

By HARSHA V

•

Oct 17, 2023

ok