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