返回到 Supervised Machine Learning: Classification

学生对 IBM 提供的 Supervised Machine Learning: Classification 的评价和反馈

4.8

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423 个评分

课程概述

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....

51 - Supervised Machine Learning: Classification 的 75 个评论（共 86 个）

创建者 Sevinchbonu K

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Mar 27, 2025

menga juda yoqdi

创建者 Augusto P

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Aug 13, 2023

EXCELLENT COURSE

创建者 Keshav U

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Jun 9, 2022

Excellent course

创建者 Gabriel R C P

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Mar 24, 2022

Great course!

创建者 Nandana A

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Jan 25, 2021

Learned a lot

创建者 Abdul Q

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Sep 20, 2023

Best Course

创建者 Victor M C

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Jul 27, 2024

buen Curso

创建者 Cui Y

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Jan 13, 2022

Thank you!

创建者 Nasibkamal A

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May 3, 2024

very good

创建者 Amin D

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Jan 30, 2023

Thanks!

创建者 Maram A A

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Dec 28, 2022

useful

创建者 Saeid S S

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Apr 23, 2022

great

创建者 Pierluigi A

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Dec 27, 2020

great

创建者 Rahul C

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Aug 15, 2025

good

创建者 Nilesh K

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Jan 17, 2024

Good

创建者 Rohit P

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Oct 16, 2021

Best

创建者 Rishikesh K

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Jul 25, 2025

nil

创建者 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...

创建者 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.

创建者 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.

创建者 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.

创建者 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!