Predictive Analytics with Microsoft Azure Machine Learning, Second Edition

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition

by Roger Barga, Valentine Fontama, Wee Hyong Tok
Released August 2015
Publisher(s): Apress
ISBN: 9781484212004

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Book description

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models.

The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applying machine learning, evaluating the models, and deploying them as web services.

Learn how you can quickly build and deploy sophisticated predictive models with the new Azure Machine Learning from Microsoft.

What’s New in the Second Edition?

Five new chapters have been added with practical detailed coverage of:

  • Python Integration – a new feature announced February 2015
  • Data preparation and feature selection
  • Data visualization with Power BI
  • Recommendation engines
  • Selling your models on Azure Marketplace
    •

    Table of contents

    1. Cover
    2. Title
    3. Copyright
    4. Contents at a Glance
    5. Contents
    6. About the Authors
    7. About the Techincal reviewers
    8. Foreword
    9. Acknowledgments
    10. Introduction
    11. Part I: Introducing Data Science and Microsoft Azure Machine Learning
      1. Chapter 1: Introduction to Data Science
        1. What is Data Science?
        2. Analytics Spectrum
          1. Descriptive Analysis
          2. Diagnostic Analysis
          3. Predictive Analysis
          4. Prescriptive Analysis
        3. Why Does It Matter and Why Now?
          1. Data as a Competitive Asset
          2. Increased Customer Demand
          3. Increased Awareness of Data Mining Technologies
          4. Access to More Data
          5. Faster and Cheaper Processing Power
          6. The Data Science Process
        4. Common Data Science Techniques
          1. Classification Algorithms
          2. Clustering Algorithms
          3. Regression Algorithms
          4. Simulation
          5. Content Analysis
          6. Recommendation Engines
        5. Cutting Edge of Data Science
          1. The Rise of Ensemble Models
        6. Summary
        7. Bibliography
      2. Chapter 2: Introducing Microsoft Azure Machine Learning
        1. Hello, Machine Learning Studio!
        2. Components of an Experiment
        3. Introducing the Gallery
        4. Five Easy Steps to Creating a Training Experiment
          1. Step 1: Getting the Data
          2. Step 2: Preprocessing the Data
          3. Step 3: Defining the Features
          4. Step 4: Choosing and Applying Machine Learning Algorithms
          5. Step 5: Predicting Over New Data
        5. Deploying Your Model in Production
          1. Creating a Predictive Experiment
          2. Publishing Your Experiment as a Web Service
          3. Accessing the Azure Machine Learning Web Service
        6. Summary
      3. Chapter 3: Data Preparation
        1. Data Cleaning and Processing
          1. Getting to Know Your Data
          2. Missing and Null Values
          3. Handling Duplicate Records
          4. Identifying and Removing Outliers
          5. Feature Normalization
          6. Dealing with Class Imbalance
        2. Feature Selection
        3. Feature Engineering
          1. Binning Data
          2. The Curse of Dimensionality
        4. Summary
      4. Chapter 4: Integration with R
        1. R in a Nutshell
        2. Building and Deploying Your First R Script
        3. Using R for Data Preprocessing
        4. Using a Script Bundle (ZIP)
        5. Building and Deploying a Decision Tree Using R
        6. Summary
      5. Chapter 5: Integration with Python
        1. Overview
        2. Python Jumpstart
        3. Using Python in Azure ML Experiments
        4. Using Python for Data Preprocessing
          1. Combining Data using Python
          2. Handling Missing Data Using Python
          3. Feature Selection Using Python
          4. Running Python Code in an Azure ML Experiment
        5. Summary
    12. Part II: Statistical and Machine Learning Algorithms
      1. Chapter 6: Introduction to Statistical and Machine Learning Algorithms
        1. Regression Algorithms
          1. Linear Regression
          2. Neural Networks
          3. Decision Trees
          4. Boosted Decision Trees
        2. Classification Algorithms
          1. Support Vector Machines
          2. Bayes Point Machines
        3. Clustering Algorithms
        4. Summary
    13. Part III: Practical Applications
      1. Chapter 7: Building Customer Propensity Models
        1. The Business Problem
        2. Data Acquisition and Preparation
          1. Data Analysis
        3. Training the Model
        4. Model Testing and Validation
        5. Model Performance
        6. Prioritizing Evaluation Metrics
        7. Summary
      2. Chapter 8: Visualizing Your Models with Power BI
        1. Overview
        2. Introducing Power BI
        3. Three Approaches for Visualizing with Power BI
        4. Scoring Your Data in Azure Machine Learning and Visualizing in Excel
        5. Scoring and Visualizing Your Data in Excel
        6. Scoring Your Data in Azure Machine Learning and Visualizing in powerbi.com
          1. Loading Data
          2. Building Your Dashboard
        7. Summary
      3. Chapter 9: Building Churn Models
        1. Churn Models in a Nutshell
        2. Building and Deploying a Customer Churn Model
          1. Preparing and Understanding Data
          2. Data Preprocessing and Feature Selection
          3. Classification Model for Predicting Churn
          4. Evaluating the Performance of the Customer Churn Models
        3. Summary
      4. Chapter 10: Customer Segmentation Models
        1. Customer Segmentation Models in a Nutshell
        2. Building and Deploying Your First K-Means Clustering Model
          1. Feature Hashing
          2. Identifying the Right Features
          3. Properties of K-Means Clustering
        3. Customer Segmentation of Wholesale Customers
          1. Loading the Data from the UCI Machine Learning Repository
          2. Using K-Means Clustering for Wholesale Customer Segmentation
          3. Cluster Assignment for New Data
        4. Summary
      5. Chapter 11: Building Predictive Maintenance Models
        1. Overview
        2. Predictive Maintenance Scenarios
        3. The Business Problem
        4. Data Acquisition and Preparation
          1. The Dataset
          2. Data Loading
          3. Data Analysis
        5. Training the Model
        6. Model Testing and Validation
        7. Model Performance
        8. Techniques for Improving the Model
          1. Upsampling and Downsampling
        9. Model Deployment
          1. Creating a Predictive Experiment
          2. Publishing Your Experiment as a Web Service
        10. Summary
      6. Chapter 12: Recommendation Systems
        1. Overview
        2. Recommendation Systems Approaches and Scenarios
        3. The Business Problem
        4. Data Acquisition and Preparation
          1. The Dataset
        5. Training the Model
        6. Model Testing and Validation
        7. Summary
      7. Chapter 13: Consuming and Publishing Models on Azure Marketplace
        1. What Are Machine Learning APIs?
        2. How to Use an API from Azure Marketplace
        3. Publishing Your Own Models in Azure Marketplace
        4. Creating and Publishing a Web Service for Your Machine Learning Model
          1. Creating Scoring Experiment
          2. Publishing Your Experiment as a Web Service
        5. Obtaining the API Key and the Details of the OData Endpoint
        6. Publishing Your Model as an API in Azure Marketplace
        7. Summary
      8. Chapter 14: Cortana Analytics
        1. What Is the Cortana Analytics Suite?
        2. Capabilities of Cortana Analytics Suite
        3. Example Scenario
        4. Summary
    14. Index

    Product information

    • Title: Predictive Analytics with Microsoft Azure Machine Learning, Second Edition
    • Author(s): Roger Barga, Valentine Fontama, Wee Hyong Tok
    • Release date: August 2015
    • Publisher(s): Apress
    • ISBN: 9781484212004