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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ensemble-models-in-machine-learning-with-python/
课程评论：没有评论
课程名称：使用Python的集成模型在机器学习中的应用 课程概述：本实用课程专注于使用Python编程语言进行监督机器学习中的集成模型。集成模型是一种将多个模型组合在一起的特定类型的机器学习模型。其基本思想是通过多模型的结合，提高单一模型的性能，既可以提高稳定性（即降低方差），又可以提高准确性（即降低偏差）。本课程将详细讲解最常见的集成模型，例如随机森林和梯度提升决策树。此外，还将探讨投票和堆叠等更复杂的集成模型，它们能够进一步提升模型性能。 通过本课程，您将学习到： - 偏差-方差权衡的概念及其处理方法 - 装袋法及一些装袋模型（如随机森林） - 提升法及一些提升模型（如XGBoost或AdaBoost） - 投票和堆叠方法 课程中的每一课都以简短的介绍开始，并以Python编程语言和其强大的scikit-learn库中的实用示例结束。环境将使用Jupyter，这是数据科学行业的标准工具，所有Jupyter笔记本均可下载。本课程是“使用Python的监督机器学习”在线课程的一部分，因此您会发现一些内容已包含在更大型课程中。
In this practical course, we are going to focus on ensemble models in supervised machine learning using Python programming language.Ensemble models are a particular kind of machine learning model that mixes several models together. The general idea is that a team of models is able to increase the performance of a single one, both in terms of stability (i.e. variance) and in terms of accuracy (i.e. bias). The most common ensemble models are Random Forests and Gradient Boosting Decision Trees, which are explained extensively in the lessons of this course. Other types of ensemble models are voting and stacking, which are more complex procedures that are able to increase the performance of a model.With this course, you are going to learn:What bias-variance tradeoff is and how to deal with itBagging and some bagging models (like Random Forest)Boosting and some boosting models (Like XGBoost or AdaBoost)VotingStackingAll 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 larger course.
