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Azure Machine Learning for the Business

Course Overview
This course gives you an introduction to Azure Machine Learning (ML) and associated technologies, such as R, Power BI and RStudio. This course begins by covering an introduction to Azure ML, and then we walk through Azure ML as if we were working our way through a Data Science Project. Once the Data Science project is complete, we will look at how you can set up and report on the Azure ML modelling process with Power BI.

Course Details
  • Author: Jen Stirrup
  • Length: 2 hours, 48 minutes, 6 seconds
  • Lab: 0 hours, 0 minutes, 0 seconds
  • Published: 2017-03-06
  • Level: 300


Course Objectives

What You Will Learn
  • How to connect to offline and online data sources in AzureML
  • Cleaning your Data for better results in Data Science
  • Solving marketing problems with AzureML
  • Understand your customers better with AzureML
  • Reviewing and interpreting your model results
  • How to use Power BI to show the results
  • Using models in production

Course Modules

Module 1: Introduction to AzureML

Introduction to AzureML from the Business perspective.  This will cover how you, as a Business Intelligence professional, can springboard to AzureML. 

     Introduction to Machine Learning concepts and vocabulary

     Introduction to AzureML technology

Module 2: Connecting to Data

Similar to module 1, however we'll focus on connecting to data sources, both online and offline. 

     Offline Data Sources

     Online Data Sources

     Internal and External Data Sources

Module 3: Cleaning Data

Walking through missing and dirty data, what it is and how to clean it up using AzureML .

     Dirty Data

     Manipulating Data

     Sampling Data

     Scaling and Reducing Data

Module 4: Creating a Targeted Mailing List With Decision Trees

How can you use AzureML to create a Targeted Mailing List? This module will show you how.

     Process: CRISP-DM

     Decision Trees

     Interpreting Your Results

Module 5: Understanding your Customers With Clustering

Let’s use AzureML to create clusters for our customers

     Process: CRISP-DM

     Clustering

     Interpreting Your Results

Module 6: Creating a Market Basket Analysis

How can you use AzureML to create a market basket analysis using Association Rules? This module will show you how, using R.

     Process: CRISP-DM

     Market Basket Analysis

     Interpreting Your Results

Module 7: Forecasting Sales

How can you use AzureML to forecasting sales? This module will show you how, using regression

     Process: CRISP-DM

     Regression

     Interpreting Your Results

Module 8: AzureML and R

How can you use AzureML and R together to analyse your data? In this module, we have an introduction to R and RStudio in order to analyse our data in more depth.

     R

     RStudio

     Interpreting Your Results

Module 9: AzureML Model Deployment

How can you use AzureML in a production environment?

     AzureML model deployment

     Accessing the AzureML model

Module 10: AzureML and Power BI

How can you use AzureML and Power BI together to analyse your data? In this module, we have an introduction to Power BI to show the results of our AzureML efforts

     Excel

     Power BI

     Interpreting Your Results

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