返回到 Supervised Machine Learning: Classification

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

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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....

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

创建者 Poke v

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May 15, 2022

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

创建者 Ashraf Z

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Jul 24, 2023

Excellent content. Great, but intense instruction/videos.

创建者 Michael M

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Jun 15, 2022

the material in the last week felt rushed

创建者 Venkadesh R

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Sep 26, 2024

Really worthable

创建者 AMAR C

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Mar 3, 2024

Great con

创建者 Muhammad T K

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Aug 14, 2025

good

创建者 Mohamed E

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Jan 7, 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.