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所在平台: Udemy |
课程主页: https://www.udemy.com/course/feature-selection-for-machine-learning-in-python/
课程评论：没有评论
课程名称：Python中的机器学习特征选择 课程概述：本课程旨在通过实践教授如何使用Python编程语言进行机器学习的特征选择方法。选择最佳特征集对于机器学习项目的成功至关重要。特征过多可能导致模型无法正确学习信息，而特征过少则可能不足以传达重要信息。每个模型对学习特征的需求各不相同，因此选择特征十分重要。如果您想要一个稳定和高效的模型，选择合适数量的变量是数据科学工作流程中最重要的步骤之一。 在本课程中，您将学习： - 回归模型的特征选择 - 分类模型的特征选择 - 递归特征消除 - 结合交叉验证的递归特征消除 每节课都以简短的介绍开始，并以Python编程语言及其强大的scikit-learn库中的实际示例结束。课程将使用数据科学行业标准的Jupyter环境，所有Jupyter笔记本均可下载。此课程是我在Coursera上提供的“Python监督机器学习”在线课程的一部分，因此您会发现一些已包含在更全面课程中的内容。
In this practical course, we are going to focus on the feature selection approaches for machine learning using Python programming language.Selecting the best set of features is crucial for the success of a machine learning project. Too many features will not make the model learn the information properly while using a few features won't carry enough information. Each model has its own needs regarding the features to learn from, so it's important to select them properly. If you want a stable and efficient model, selecting the right number of variables is one of the most important steps in your data science pipeline.With this course, you are going to learn:Feature selection for regression modelsFeature selection for classification modelsRecursive Feature Elimination Recursive Feature Elimination with cross-validationAll the lessons of this course start with a brief introduction and end with a practical example in Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry. All the Jupyter notebooks are downloadable.This course is part of my Supervised Machine Learning in Python online course, so you'll find some lessons that are already included in the more extensive course.
