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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-mining-for-business-analytics/
课程评论：没有评论
**课程总结：面向商业分析的数据挖掘** 本课程“面向商业分析的数据挖掘”旨在提供对机器学习和数据挖掘在商业分析领域应用的全面概述，重点介绍其在高层次潜力以及具体应用。 课程主要内容包括： 1. **数据挖掘流程核心思想**：涵盖了数据挖掘、大数据、商业分析和商业智能等关键概念的定义。 2. **探索性数据分析基础**：通过 R 语言介绍探索性数据分析（EDA）的基本方法。 3. **复杂数据集可视化**：详细探讨了商业分析和高级数据分析中常用的可视化技术。 4. **多元线性回归在住房估价中的应用**：以住房估价为例，演示数据挖掘过程的基本步骤。 5. **随机森林和自然语言处理在商店折扣分析中的应用**：探讨了决策树和随机森林等机器学习方法，并通过商店折扣示例展示了自然语言处理和随机森林在文本丰富数据中的应用。 6. **无监督机器学习在市场篮子分析中的应用**：详细介绍了 Apriori 和关联规则等无监督机器学习方法，并以市场篮子分析为例说明了这些方法的应用。
The course content is dedicated to the applications of machine learning and data mining to business analysis. The intent is to give a high-level overview of the potential of machine learning and data mining in different areas and, more specifically, the area of business analytics. The main sections are:1. Core ideas of the data mining process - this section covers the definitions of the concepts of data mining, big data, business analytics and business intelligence.2. Basics of exploratory data analysis - this section covers EDA with R.3. Visualizing complex data sets - in this part the main visualizations used in business analytics and advanced data analysis are discussed in detail.4. Housing valuation with multiple linear regression - this section covers the basic steps of the data mining process using housing valuation example.5. Store discounts with random forest and natural language processing - in this section, the topic of machine learning methods, such as, decision trees and random forest, is examined. An example of store discounts is given to illustrate the application of natural language processing and random forest to text-rich data.6. Market basket analysis with unsupervised machine learning - in this section unsupervised machine learning methods, such as, Apriori and associative rules are examined in detail. An example with market basket analysis is given to illustrate the application of Apriori and associative rules.
