ML第6周学习小结(Ml week 6 learning summary)-其他

ML第6周学习小结(Ml week 6 learning summary)

本周收获

总结一下本周学习内容：

• 1、学习了《深入浅出Pandas》的第六章：Pandas分组聚合

  6.1概述

  6.2分组

  6.3分组对象的操作

  我的博客链接： Pandas 分组聚合 ：分组、分组对象操作

• 6.1概述
• 6.2分组
• 6.3分组对象的操作
• 我的博客链接： Pandas 分组聚合 ：分组、分组对象操作
• 2、《Python机器学习基础教程》第四章p161-p180

  分类变量

  我的博客链接：数据表示与特征工程–分类变量

  分箱、离散化、线性模型与树

  我的博客链接：数据表示与特征工程–分箱、离散化、线性模型与树

  交互特征与多项式特征

  我的博客链接：数据表示与特征工程–交互特征与多项式特征

  单变量非线性变换

  我的博客链接： 数据表示与特征工程–单变量非线性变换

• 分类变量

  我的博客链接：数据表示与特征工程–分类变量

• 我的博客链接：数据表示与特征工程–分类变量
• 分箱、离散化、线性模型与树

  我的博客链接：数据表示与特征工程–分箱、离散化、线性模型与树

• 我的博客链接：数据表示与特征工程–分箱、离散化、线性模型与树
• 交互特征与多项式特征

  我的博客链接：数据表示与特征工程–交互特征与多项式特征

• 我的博客链接：数据表示与特征工程–交互特征与多项式特征
• 单变量非线性变换

  我的博客链接： 数据表示与特征工程–单变量非线性变换

• 我的博客链接： 数据表示与特征工程–单变量非线性变换

下周计划

我的下周flag是：

• 1、《深入浅出Pandas》的第六章：Pandas分组聚合

  6.4 聚合统计
  6.5 数据分箱

• 6.4 聚合统计
• 6.5 数据分箱
• 2、《Python机器学习基础教程》第四章：数据表示与特征工程

  自动化特征选择

  单变量统计
  基于模型的特征选择
  迭代特征选择

  利用专家知识

• 自动化特征选择

  单变量统计
  基于模型的特征选择
  迭代特征选择

• 单变量统计
• 基于模型的特征选择
• 迭代特征选择
• 利用专家知识

Harvest this week

Summarize the learning content of this week:

• 1. I learned Chapter 6 of pandas in simple terms: Pandas grouping aggregation
  6.1 general
  6.2 grouping
  6.3 operation of grouped objects
  My blog link: Pandas grouping aggregation: grouping, grouping object operation
• 6.1 general
• 6.2 grouping
• 6.3 operation of grouped objects
• My blog link: Pandas grouping aggregation: grouping, grouping object operation
• 2. Chapter 4 p161-p180 of basic Python machine learning tutorial
  Categorical variable
  My blog link: data representation and Feature Engineering — Classification variables
  Box division, discretization, linear model and tree
  My blog link: data representation and Feature Engineering — box division, discretization, linear model and tree
  Interactive features and polynomial features
  My blog link: data representation and Feature Engineering — interactive features and polynomial features
  Univariate nonlinear transformation
  My blog link: data representation and Feature Engineering — univariate nonlinear transformation
• Categorical variable
  My blog link: data representation and Feature Engineering — Classification variables
• My blog link: data representation and Feature Engineering — Classification variables
• Box division, discretization, linear model and tree
  My blog link: data representation and Feature Engineering — box division, discretization, linear model and tree
• My blog link: data representation and Feature Engineering — box division, discretization, linear model and tree
• Interactive features and polynomial features
  My blog link: data representation and Feature Engineering — interactive features and polynomial features
• My blog link: data representation and Feature Engineering — interactive features and polynomial features
• Univariate nonlinear transformation
  My blog link: data representation and Feature Engineering — univariate nonlinear transformation
• My blog link: data representation and Feature Engineering — univariate nonlinear transformation

Plan for next week

My next week’s flag is:

• 1. Chapter 6 of pandas in simple terms: Pandas grouping aggregation
  6.4 aggregation statistics
  6.5 data distribution box
• 6.4 aggregation statistics
• 6.5 data distribution box
• 2. Chapter 4 of basic tutorial of Python machine learning: data representation and Feature Engineering
  Automatic feature selection
  Univariate statistics
  Model based feature selection
  Iterative feature selection
  Using expert knowledge
• Automatic feature selection
  Univariate statistics
  Model based feature selection
  Iterative feature selection
• Univariate statistics
• Model based feature selection
• Iterative feature selection
• Using expert knowledge