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Learner Reviews & Feedback for Supervised Machine Learning: Classification by IBM Skills Network

4.9
stars
221 ratings

About the Course

This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes. By the end of this course you should be able to: -Differentiate uses and applications of classification and classification ensembles -Describe and use logistic regression models -Describe and use decision tree and tree-ensemble models -Describe and use other ensemble methods for classification -Use a variety of error metrics to compare and select the classification model that best suits your data -Use oversampling and undersampling as techniques to handle unbalanced classes in a data set   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification 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

NR

Feb 21, 2022

Great course, well structured. The presentation of the different methods is very clear and well separated to understand the differences. A good understanding of classifiers is gained from this course.

AP

Feb 28, 2021

Superb ,detailed, well explained, lots of hands on training through labs and most of the major alogrithms are covered!

Keep up the good work. You guys are helping the community a lot :D

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26 - 50 of 51 Reviews for Supervised Machine Learning: Classification

By Kevin P

Mar 21, 2022

this course taught me a lot even after being a practioner for 10+ years!

By george s

Aug 30, 2021

One of the best courses offered by IBM and coursera, 100% recommended.

By Marwan K

Nov 23, 2021

Thank you Coursera.

Thank you IBM

Thank you to all instructors.

By Luis P S

May 24, 2021

Always a pleasure learning new ML skills through this course!

By Javier I T V

Feb 4, 2023

Amazing. Full of content, activities. A lot to learn

By Yohanes S

Jun 30, 2022

Great! Helps me build my career path in Data Science

By Wissam Z

Aug 22, 2021

Best professional machine learning course

By Hayyan A

Mar 22, 2022

it is helpful and wonderful

By Vishal J

Dec 4, 2020

Changed my viewpoint

By Keshav U

Jun 10, 2022

Excellent course

By Gabriel R C P

Mar 24, 2022

Great course!

By Nandana A

Jan 25, 2021

Learned a lot

By Cui Y

Jan 13, 2022

Thank you!

By Amin D

Jan 30, 2023

Thanks!

By Maram A A

Dec 28, 2022

useful

By Saeid S S

Apr 23, 2022

great

By Pierluigi A

Dec 27, 2020

great

By Rohit P

Oct 16, 2021

Best

By MAURICIO C

Apr 17, 2021

there is a lot of information with machine learning strategies and explain how to think in front of results. Super Course ! JSON files made me confusion, it mentions notebook jupiter files but not.

By Cristiano C

Jan 18, 2021

Interesting Course, sometimes it skips some arguments that should be, imho, studied a bit deeper (i.e. UP/DOWN sampling), for the rest it's a great course with a great teacher!

By Keyur U

Dec 24, 2020

This course is has a detailed explanation on each and every aspect of classification.

By Carlos M

May 15, 2022

Maybe tasks within the weeks lesson could be helpful to build a powerful knowledge.

By Michael M

Jun 15, 2022

the material in the last week felt rushed

By Mohamed S E E E

Jan 8, 2023

The instructor was very good in drilling deep in the code snippets, explaining what every line does clearly, but on theoretic side of every algorithm, I see the handling was poor, lacks the depth and clarity, many times I looked at an external sources to understand how a model works.

By Meith N

Jul 15, 2021

Need to cover some basic information and examples too cause directly start from complex examples in the code section