Flashield's Blog

Just For My Daily Diary

Flashield's Blog

Just For My Daily Diary

Machine Learning

03.exercise-group-by-having-count【练习:GROUP By、Having和COUNT】

This notebook is an exercise in the SQL course. You can reference the tutorial at this link. Introduction 介绍 Queries with GROUP BY can be powerful. There are many small things that can trip you up (like the order of the clauses), but it will start to feel natural once you’ve done it a few […]

03.course-group-by-having-count【GROUP By、Having和COUNT】

Introduction 介绍 Now that you can select raw data, you’re ready to learn how to group your data and count things within those groups. This can help you answer questions like: 现在您可以选择原始数据,您已经准备好学习如何对数据进行分组并对这些组中的内容进行计数。 这可以帮助您回答以下问题: How many of each kind of fruit has our store sold? 我们店每种水果销售了多少? How many species of animal has the vet office treated? […]

02.exercise-select-from-where【练习:SELECT、FROM和WHERE】

This notebook is an exercise in the SQL course. You can reference the tutorial at this link. Introduction 介绍 Try writing some SELECT statements of your own to explore a large dataset of air pollution measurements. 尝试编写一些自己的 SELECT 语句来探索空气污染测量的大型数据集。 Run the cell below to set up the feedback system. 运行下面的单元格来设置反馈系统。 # Set up feedback system […]

02.course-select-from-where【SELECT、FROM和WHERE】

Introduction 介绍 Now that you know how to access and examine a dataset, you’re ready to write your first SQL query! As you’ll soon see, SQL queries will help you sort through a massive dataset, to retrieve only the information that you need. 现在您已经知道如何访问和检查数据集,您就可以编写您的第一个 SQL 查询了! 您很快就会看到,SQL 查询将帮助您对海量数据集进行排序,以仅检索您需要的信息。 We’ll begin by using the keywords SELECT, […]

01.exercise-getting-started-with-sql-and-bigquery【练习:开始使用SQL和BQ】

This notebook is an exercise in the SQL course. You can reference the tutorial at this link. Introduction 介绍 The first test of your new data exploration skills uses data describing crime in the city of Chicago. 对您的新数据探索技能的首次测试,使用描述芝加哥市犯罪的数据。 Before you get started, run the following cell. It sets up the automated feedback system to review […]

01.course-getting-started-with-sql-and-bigquery【开始使用SQL和BQ】

Introduction 介绍 Structured Query Language, or SQL, is the programming language used with databases, and it is an important skill for any data scientist. In this course, you’ll build your SQL skills using BigQuery, a web service that lets you apply SQL to huge datasets. 结构化查询语言(或 SQL)是用于数据库的编程语言,对于任何数据科学家来说都是一项重要技能。 在本课程中,您将使用 BigQuery 培养您的 SQL 技能,这是一项可让您将 SQL 应用于大型数据集的 Web […]

06.exercise-target-encoding【练习:目标编码】

This notebook is an exercise in the Feature Engineering course. You can reference the tutorial at this link. Introduction 介绍 In this exercise, you’ll apply target encoding to features in the Ames dataset. 在本练习中,您将对 Ames 数据集中的特征应用目标编码。 Run this cell to set everything up! 运行这个单元格来设置一切! # Setup feedback system from learntools.core import binder binder.bind(globals()) from learntools.feature_engineering_new.ex6 […]

06.course-target-encoding【目标编码】

Introduction 介绍 Most of the techniques we’ve seen in this course have been for numerical features. The technique we’ll look at in this lesson, target encoding, is instead meant for categorical features. It’s a method of encoding categories as numbers, like one-hot or label encoding, with the difference that it also uses the target to […]

05.exercise-principal-component-analysis【练习:主成分分析】

This notebook is an exercise in the Feature Engineering course. You can reference the tutorial at this link. Introduction 介绍 In this exercise, you’ll work through several applications of PCA to the Ames dataset. 在本练习中,您将在 Ames 数据集上完成 PCA 的多种应用。 Run this cell to set everything up! 运行这个单元格来设置一切! # Setup feedback system from learntools.core import binder […]

05.course-principal-component-analysis【主成分分析】

Introduction 介绍 In the previous lesson we looked at our first model-based method for feature engineering: clustering. In this lesson we look at our next: principal component analysis (PCA). Just like clustering is a partitioning of the dataset based on proximity, you could think of PCA as a partitioning of the variation in the data. […]

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