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

4.7

stars

557 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

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really good course, content is rich with good machine learning concepts

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Very well presented. This is without doubt the best series for Machine Learning on Coursera.

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101 - 111 of 111 Reviews for Supervised Machine Learning: Regression

By Iddi A A

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Dec 11, 2020

Excellent

By Juhi S

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May 20, 2022

GOOD

By YASH A

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Apr 22, 2021

Nice

By Evangelos N

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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 The course is outdated and not very professional and this is obvious in various examples, to name a few: a) There are some syntax errors in the notebooks.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

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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

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Oct 17, 2023