基于机器学习的客户流失预警分析-阿里云大学

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我的大学

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客户流失及分析方法

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机器学习及决策树介绍

介绍机器学习的概念及常见算法分类，决策树算法的基本原理。

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如何构建一棵决策树（上）

基于一个小的样本集，通过手工计算，创建一棵决策树。

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如何构建一棵决策树（下）

基于一个小的样本集，通过手工计算，创建一棵决策树。

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决策树算法总结

介绍及总结ID3、C45、C50、CART等决策树算法的特点。

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集成学习

介绍什么是集成学习，详细介绍Bagging、Boosting的实现原理及逻辑。

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随机森林RandomForest

介绍随机森林算法的实现原理，Smart Bagging等。

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机器学习几个重要的概念

介绍机器学习的重要概念，包括特征工程、模型评估、解释与泛化、CRISP-DM流程等。

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客户流失预警的实现流程

从数据准备到结果应用，五步完成客户流失预警。

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阿里云机器学习PAI基本介绍

介绍本次实验中用到的阿里云机器学习PAI，上手简单，算法丰富，支持深度学习，一站式的体验。

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阿里云机器学习PAI操作演示

机器学习PAI的基本操作演示，开通服务、创建项目等，并使用周知数据集IRIS进行分类模型训练。

开始学习

项目实战——通信公司客户流失预警分析

实验项目介绍，包括项目的背景、目标、已有资源等，并介绍了整个项目中关键的技术点，以及最终的项目效果。

开始学习