Custom cover image
Predictive Analytics with Microsoft Azure Machine Learning / by Roger Barga, Valentine Fontama, Wee Hyong Tok
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink BücherPublisher: Berkeley, CA ; s.l. : Apress, 2015Edition: Second EditionDescription: Online-Ressource (250 p, online resource)ISBN:- 9781484212004
- 004
- QA75.5-76.95
Contents:
Summary: 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 MarketplacePPN: PPN: 1657851796Package identifier: Produktsigel: ZDB-2-CWD
Contents at a Glance; Contents; About the Authors; About the Technical Reviewers; Acknowledgments; Foreword; Introduction; Part I: Introducing Data Science and Microsoft Azure Machine Learning ; Chapter 1: Introduction to Data Science; What is Data Science?; Analytics Spectrum ; Descriptive Analysis; Diagnostic Analysis; Predictive Analysis; Prescriptive Analysis; Why Does It Matter and Why Now?; Data as a Competitive Asset ; Increased Customer Demand ; Increased Awareness of Data Mining Technologies ; Access to More Data; Faster and Cheaper Processing Power
The Data Science Process Common Data Science Techniques ; Classification Algorithms; Clustering Algorithms ; Regression Algorithms; Simulation ; Content Analysis; Recommendation Engines ; Cutting Edge of Data Science; The Rise of Ensemble Models; Real-World Applications of Ensemble Models; Building an Ensemble Model; Summary; Bibliography; Chapter 2: Introducing Microsoft Azure Machine Learning; Hello, Machine Learning Studio!; Components of an Experiment; Introducing the Gallery; Five Easy Steps to Creating a Training Experiment; Step 1: Getting the Data
Step 2: Preprocessing the Data Step 3: Defining the Features; Step 4: Choosing and Applying Machine Learning Algorithms ; Step 5: Predicting Over New Data; Deploying Your Model in Production; Creating a Predictive Experiment ; Publishing Your Experiment as a Web Service; Accessing the Azure Machine Learning Web Service ; Summary; Chapter 3: Data Preparation; Data Cleaning and Processing; Getting to Know Your Data; Missing and Null Values; Handling Duplicate Records; Identifying and Removing Outliers; Feature Normalization; Dealing with Class Imbalance; Feature Selection
Feature Engineering Binning Data; The Curse of Dimensionality; Summary; Chapter 4: Integration with R; R in a Nutshell ; Building and Deploying Your First R Script; Using R for Data Preprocessing ; Using a Script Bundle (ZIP) ; Building and Deploying a Decision Tree Using R; Summary; Chapter 5: Integration with Python; Overview ; Python Jumpstart ; Using Python in Azure ML Experiments ; Using Python for Data Preprocessing ; Combining Data using Python; Handling Missing Data Using Python; Feature Selection Using Python; Running Python Code in an Azure ML Experiment; Summary
Part II: Statistical and Machine Learning Algorithms Chapter 6: Introduction to Statistical and Machine Learning Algorithms; Regression Algorithms; Linear Regression ; Neural Networks ; Decision Trees ; Boosted Decision Trees; Classification Algorithms ; Support Vector Machines ; Bayes Point Machines ; Clustering Algorithms ; Summary; Part III: Practical Applications ; Chapter 7: Building Customer Propensity Models; The Business Problem ; Data Acquisition and Preparation ; Data Analysis; More Data Treatment; Feature Selection; Training the Model; Model Testing and Validation
Model Performance
No physical items for this record