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[FreeCoursesOnline.Me] [Packt] Python, SQL, Tableau Integrating Python, SQL, and Tableau [FCO]

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种子名称: [FreeCoursesOnline.Me] [Packt] Python, SQL, Tableau Integrating Python, SQL, and Tableau [FCO]
文件类型: 视频
文件数目: 63个文件
文件大小: 1.67 GB
收录时间: 2019-10-19 15:41
已经下载: 3
资源热度: 177
最近下载: 2024-6-30 14:51

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[FreeCoursesOnline.Me] [Packt] Python, SQL, Tableau Integrating Python, SQL, and Tableau [FCO].torrent
  • 01.Introduction/0101.What Does the Course Cover.mp439.5MB
  • 02.What is software integration/0201.Properties and Definitions Data, Servers, Clients, Requests and Responses.mp430.27MB
  • 02.What is software integration/0202.Properties and Definitions Data Connectivity, APIs, and Endpoints.mp456.2MB
  • 02.What is software integration/0203.Further Details on APIs.mp455.78MB
  • 02.What is software integration/0204.Text Files as Means of Communication.mp429.46MB
  • 02.What is software integration/0205.Definitions and Applications.mp433.9MB
  • 03.Setting up the working environment/0301.Setting Up the Environment - An Introduction (Do Not Skip, Please)!.mp44.92MB
  • 03.Setting up the working environment/0302.Why Python and why Jupyter.mp435.26MB
  • 03.Setting up the working environment/0303.Installing Anaconda.mp430.36MB
  • 03.Setting up the working environment/0304.The Jupyter Dashboard - Part 1.mp46.77MB
  • 03.Setting up the working environment/0305.The Jupyter Dashboard - Part 2.mp414.39MB
  • 03.Setting up the working environment/0306.Installing sklearn.mp47.93MB
  • 04.What's next in the course/0401.Up Ahead.mp425.84MB
  • 04.What's next in the course/0402.Real-Life Example Absenteeism at Work.mp425.54MB
  • 04.What's next in the course/0403.Real-Life Example The Dataset.mp425.81MB
  • 05.Preprocessing/0501.Data Sets in Python.mp413.03MB
  • 05.Preprocessing/0502.Data at a Glance.mp439.36MB
  • 05.Preprocessing/0503.A Note on Our Usage of Terms with Multiple Meanings.mp420.06MB
  • 05.Preprocessing/0504.Picking the Appropriate Approach for the Task at Hand.mp410.93MB
  • 05.Preprocessing/0505.Removing Irrelevant Data.mp434.34MB
  • 05.Preprocessing/0506.Examining the Reasons for Absence.mp420.25MB
  • 05.Preprocessing/0507.Splitting a Column into Multiple Dummies.mp446.7MB
  • 05.Preprocessing/0508.Dummy Variables and Their Statistical Importance.mp45.75MB
  • 05.Preprocessing/0509.Grouping - Transforming Dummy Variables into Categorical Variables.mp438.49MB
  • 05.Preprocessing/0510.Concatenating Columns in Python.mp418.38MB
  • 05.Preprocessing/0511.Changing Column Order in Pandas DataFrame.mp46.94MB
  • 05.Preprocessing/0512.Implementing Checkpoints in Coding.mp413.01MB
  • 05.Preprocessing/0513.Exploring the Initial Date Column.mp426.59MB
  • 05.Preprocessing/0514.Using the Date Column to Extract the Appropriate Month Value.mp424.11MB
  • 05.Preprocessing/0515.Introducing Day of the Week.mp415.08MB
  • 05.Preprocessing/0516.Further Analysis of the DataFrame Next 5 Columns.mp414.11MB
  • 05.Preprocessing/0517.Further Analysis of the DaraFrame Education, Children, Pets.mp418.79MB
  • 05.Preprocessing/0518.A Final Note on Preprocessing.mp422.05MB
  • 06.Machine Learnings/0601.Exploring the Problem from a Machine Learning Point of View.mp425.5MB
  • 06.Machine Learnings/0602.Creating the Targets for the Logistic Regression.mp434.39MB
  • 06.Machine Learnings/0603.Selecting the Inputs.mp412.26MB
  • 06.Machine Learnings/0604.A Bit of Statistical Preprocessing.mp415.62MB
  • 06.Machine Learnings/0605.Train-test Split of the Data.mp439.92MB
  • 06.Machine Learnings/0606.Training the Model and Assessing its Accuracy.mp432.65MB
  • 06.Machine Learnings/0607.Extracting the Intercept and Coefficients from a Logistic Regression.mp433.39MB
  • 06.Machine Learnings/0608.Interpreting the Logistic Regression Coefficients.mp446.47MB
  • 06.Machine Learnings/0609.Omitting the dummy variables from the Standardization.mp433.87MB
  • 06.Machine Learnings/0610.Interpreting the Important Predictors.mp428.11MB
  • 06.Machine Learnings/0611.Simplifying the Model (Backward Elimination).mp437.87MB
  • 06.Machine Learnings/0612.Testing the Machine Learning Model.mp441.45MB
  • 06.Machine Learnings/0613.How to Save the Machine Learning Model and Prepare it for Future Deployment.mp430.4MB
  • 06.Machine Learnings/0614.Creating a Module for Later Use of the Model.mp449.61MB
  • 07.Installing MySQL and Getting Acquainted with the Interface/0701.Installing MySQL.mp449.73MB
  • 07.Installing MySQL and Getting Acquainted with the Interface/0702.Setting Up a Connection.mp49.54MB
  • 07.Installing MySQL and Getting Acquainted with the Interface/0703.Introduction to the MySQL Interface.mp417.82MB
  • 08.Connecting Python and SQL/0801.Implementing the 'absenteeism_module' - Part I.mp415.57MB
  • 08.Connecting Python and SQL/0802.Implementing the 'absenteeism_module' - Part II.mp428.36MB
  • 08.Connecting Python and SQL/0803.Creating a Database in MySQL.mp433.46MB
  • 08.Connecting Python and SQL/0804.Importing and Installing 'pymysql'.mp411.17MB
  • 08.Connecting Python and SQL/0805.Creating a Connection and Cursor.mp410.44MB
  • 08.Connecting Python and SQL/0806.Creating the 'predicted_outputs' table in MySQL.mp427.4MB
  • 08.Connecting Python and SQL/0807.Running an SQL SELECT Statement from Python.mp412.54MB
  • 08.Connecting Python and SQL/0808.Transferring Data from Jupyter to Workbench - Part I.mp445.36MB
  • 08.Connecting Python and SQL/0809.Transferring Data from Jupyter to Workbench - Part II.mp432.11MB
  • 08.Connecting Python and SQL/0810.Transferring Data from Jupyter to Workbench - Part III.mp422.42MB
  • 09.Analyzing the Obtained data in Tableau/0901.Analysis in Tableau Age vs Probability.mp426.42MB
  • 09.Analyzing the Obtained data in Tableau/0902.Analysis in Tableau Reasons vs Probability.mp430.18MB
  • 09.Analyzing the Obtained data in Tableau/0903.Analysis in Tableau Transportation Expense vs Probability.mp466.99MB