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

4.8
stars
307 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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51 - 70 of 70 Reviews for Supervised Machine Learning: Classification

By Maram A A

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Dec 28, 2022

useful

By Saeid S S

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Apr 23, 2022

great

By Pierluigi A

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Dec 27, 2020

great

By Nilesh K

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Jan 17, 2024

Good

By Rohit P

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Oct 16, 2021

Best

By Rui C

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Jan 3, 2024

Everything is satisfactory except for the peer review section. The initial submission faced challenges, primarily attributed to an unfair assessment by one of the peer reviewers. Despite meeting certain requirements unequivocally, such as employing three distinct types of models, this reviewer did not allocate any points or provided an inadequate assessment without clear justification. It seems that many peers have similar experience...

By Dan M

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Jul 21, 2023

This course provided a very useful overview of a wide range of classification techniques using scikit-learn, including the best practice in using the techniques and theoretical underpinning of them. My one criticism would be the repetitive nature of the worked examples. Given the scikit-learn has a consistent format across all the different types of model, the actual coding of each example often followed the same format.

By Wlodek K

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Oct 13, 2023

Very good course, full of information. The downside is that passing tests largely require very good knowledge of English. Sometimes it is more difficult to understand the question and the proposed answers than the substantive value of the question. This applies to all courses in this package.

By MAURICIO C

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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

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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 Mihreteab T M

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Jul 18, 2023

Wonderful course but too many syntax and classification types - keeping focused and attentive helps achieve or succeed.

By Keyur U

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Dec 24, 2020

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

By Poke v

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May 15, 2022

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

By Ashraf Z

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Jul 24, 2023

Excellent content. Great, but intense instruction/videos.

By Michael M

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Jun 15, 2022

the material in the last week felt rushed

By AMAR C

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Mar 3, 2024

Great con

By Mohamed E

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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

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Jul 15, 2021

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

By Khalid M

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Apr 15, 2023

When explaining data and algorithms , it should be explained well with many visualizations and examples , it is very confusing to just show a static photo and telling many explanations on it

By Deepak R

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Jul 18, 2023

they did not have my name on my certificate