2018 最新Python3数据分析与挖掘建模实战
2018 最新Python3数据分析与挖掘建模实战2018 最新Python3数据分析与挖掘建模实战
第1章 课程介绍
第2章 数据获取
第3章 单因子探索分析与可视化
第4章 多因子探索分析
第5章 预处理理论
第6章 挖掘建模
第7章 模型评估
第8章 总结与展望
书籍+随堂源码+说明
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