- Python 编程经验。
- 具有 Numpy、Pandas 和 scikit-learn 的经验。
- 熟悉机器学习算法。
观众
- 开发 人员
- 数据科学家
- 数据分析师
特征工程是选择和转换数据以提高机器学习算法准确性的过程。它需要深入了解主题专家的数据。
这种以讲师为主导的现场培训(现场或远程)是针对那些希望应用特征工程技术以更好地处理数据和获得更好的机器学习模型的人。
在培训结束时,参与者将能够:
- 设置最佳开发环境,包括所有需要的Python包。
- 通过分析数据集的功能获得重要的见解。
- 通过调整原始数据本身来优化机器学习模型。
- 清理和转换数据集以准备机器学习。
课程格式
- 互动讲座和讨论。
- 大量的练习和练习。
- 在实时实验室环境中亲自实施。
课程自定义选项
- 要申请本课程的定制培训,请联系我们安排。
介绍
- 在模式识别、分类和回归方面构建有效的算法。
设置开发环境
- Python 库
- 在线编辑器与离线编辑器
特征工程概述
- 输入和输出变量(特征)
- 特征工程的优缺点
原始数据中遇到的问题类型
- 不干净的数据、缺失的数据等。
预处理变量
- 处理缺失数据
处理数据中的缺失值
使用类别变量
将标签转换为数字
处理分类变量中的标签
转换变量以提高预测能力
- 数字、分类、日期等。
清理数据集
Machine Learning 建模
处理数据中的异常值
- 数值变量、分类变量等
总结和结论
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