Data Science with Microsoft Azure and R Training Video

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AU$ 189 - ($ 2.203)

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A Practical Training Course That Teaches Real World Skills In this project-based Data Science with Microsoft Azure and R video tutorial series, you'll quickly have relevant skills for real-world applications. Follow along with our expert instructor in this training course to get: Concise, informative and broadcast-quality Data Science with Microsoft Azure and R training videos delivered to your desktop The ability to learn at your own pace with our intuitive, easy-to-use interface A quick grasp of even the most complex Data Science with Microsoft Azure and R subjects because they're broken into simple, easy to follow tutorial videos Practical working files further enhance the learning process and provide a degree of retention that is unmatched by any other form of Data Science with Microsoft Azure and R tutorial, online or you'll know the exact steps for your own projects. Course Fast Facts: Only 7 hours to complete this course 72 tutorial videos Expert instructors lead each course Download to any Windows PC or Mac and save for viewing off line Course is accessible 24/7 from any computer once downloaded You can study from home or at work at your own pace in your own time Course Description In this Data Science with Microsoft Azure and R training course, expert author Stephen Elston will teach you how to develop and deploy effective machine learning models in the Microsoft Azure Machine Learning (ML) environment. This course is designed for users that are familiar with R.

You will start with an overview of Azure ML, then move into an introduction to R in Azure ML. From there, Stephen will teach you about data munging and SQL in Azure ML, as well as how to use the dplyr package, install R packages in Azure ML, and reshape data with tidyr. This video tutorial also covers feature selection and dimensionality reduction, functional programming with R, and R object communications. Finally, you...

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  • 01. Introduction
    • Introduction
    • About The Author
  • 02. Overview Of Azure ML
    • Introduction To Azure ML Studio
    • Experiments And Workflows In Azure ML Studio
    • Azure ML Modules
    • Data I/O In Azure ML
    • Creating And Evaluating A First Machine Learning Model
    • Documentation And Examples
  • 03. Introduction To R In Azure ML
    • Editing, Debugging And Executing R In Azure ML
    • An Execute R Script Example
    • The Create R Model Module
  • 04. Data Science Examples
    • 0401 Overview Of Data Science Examples
    • 0402 The Data Science Process
  • 05. Data Munging In Azure ML
    • 0501 Introduction To Data Transformation And Cleaning
    • 0502 Dealing With Metadata
    • 0503 Duplicate And Missing Data
    • 0504 Standardization And Transformation
    • 0505 Errors And Outliers
    • 0506 Quantization And Categories
    • 0507 Combining Data Joins
  • 06. SQL In Azure ML
    • 0601 Introduction To Apply SQL Transformation Module
    • 0602 Apply SQL Transformation Exercise
  • 07. Using The dplyr Package
    • 0701 Intro To dplyr
    • 0702 dplyr Example - Part 1
    • 0703 dplyr Example - Part 2
  • 08. Installing R Packages In Azure ML
    • 0801 Installing R Packages
  • 09. Reshaping Data With tidyr
    • 0901 Reshaping Data With tidyr
  • 10. Time Series Data In Azure ML
    • 1001 Date-Time Classes In Azure ML
    • 1002 POSIXct Example
  • 11. The ggplot2 Package
    • 1101 Intro To ggplot2
    • 1102 ggplot2 Exercise
  • 12. Feature Selection And Dimensionality Reduction
    • 1201 Introduction To Feature Selection And Dimensionality Reduction
    • 1202 Exercise - Filter Based Feature Selection
    • 1203 Exercise - randomForest Feature Selection
    • 1204 Projection Methods for Dimensionality Reduction
  • 13. Functional Programming With R
    • 1301 Introduction To Functional Programming With R
    • 1302 Functional Programming Example
  • 14. Regression Example
    • 1401 Introduction To Regression Example
    • 1402 Data Preparation Example
    • 1403 Examining Correlations
    • 1404 Time Series Plots
    • 1405 Understanding Features With Box Plots
    • 1406 Other Exploratory Plots
    • 1407 Feature Selection
    • 1408 Model Evaluation With Time Series Plots
    • 1409 Model Evaluation Of Residuals - Part 1
    • 1410 Model Evaluation Of Residuals - Part 2
  • 15. Regression Example - Improving the Model
    • 1501 Introduction To Improving the Model
    • 1502 Using An R Model
    • 1503 Creating A New Azure ML Model
    • 1504 Trimming Outliers
    • 1505 Optimizing Model Parameters
    • 1506 Further Improvements And Summary
  • 16. R Object Communications In Azure ML
    • 1601 Introduction To R Object Serialization
    • 1602 R Object Serialization Example
  • 17. Classification Example
    • 1701 Introduction To Classification Example
    • 1702 Data Preparation - Part 1
    • 1703 Data Preparation - Part 2
    • 1704 Exploring The Data
    • 1705 Balance Cases
    • 1706 Feature Selection
    • 1707 Building Initial Models
    • 1708 Model Evaluation
    • 1709 First R Model
    • 1710 Improving the R Model
    • 1711 Summary
  • 18. Azure ML Web Services
    • 1801 Overview Of Publishing Azure ML Models As Web Services
    • 1802 Creating An Azure ML Web Service
    • 1803 Updating An Azure ML Web Service
    • 1804 R Model Publishing
    • 1805 Summary
  • 19. Conclusion
    • 1901 Wrap-Up

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