Mastering . NET Machine Learning.

Master the art of machine learning with .NET and gain insight into real-world applicationsAbout This Book Based on .NET framework 4.6.1, includes examples on ASP.NET Core 1.0 Set up your business application to start using machine learning techniques Familiarize the user with some of the more common...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Dixon, Jamie (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, Limited March 2016.
Temas:
Microsoft .NET Framework.
Microsoft .NET Framework
Data Processing; Programming Languages; Computers.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Copyright
  • Credits
  • About the Author
  • Acknowledgments
  • About the Reviewers
  • www.PacktPub.com
  • Table of Contents
  • Preface
  • Chapter 1: Welcome to Machine Learning Using the .NET Framework
  • What is machine learning?
  • Why .NET?
  • What version of the .NET Framework are we using?
  • Why write your own?
  • Why open data?
  • Why F♯?
  • Getting ready for machine learning
  • Setting up Visual Studio
  • Learning F♯
  • Third-party libraries
  • Math.NET
  • Accord.NET
  • Numl
  • Summary
  • Chapter 2: AdventureWorks Regression
  • Simple linear regression
  • Setting up the environment
  • Preparing the test data
  • Standard deviation
  • Pearson's Correlation
  • Linear regression
  • Math.NET
  • Regression try 1
  • Regression try 2
  • Accord.NET
  • Regression
  • Regression evaluation using RSME
  • Regression and the real world
  • Regression against actual data
  • AdventureWorks app
  • Setting up the environment
  • Updating the existing web project
  • Implementing the regression
  • Summary
  • Chapter 3: More AdventureWorks Regression
  • Introduction to multiple linear regression
  • Intro example
  • Keep adding x variables?
  • AdventureWorks data
  • Add multiple regression to our production application
  • Considerations when using multiple x variables
  • Adding a third x variable to our model
  • Logistic regression
  • Intro to logistic regression
  • Adding another x variable
  • Applying a logistic regression to AdventureWorks data
  • Categorical data
  • Attachment point
  • Analyzing results of the logistic regression
  • Adding logistic regression to the application
  • Summary
  • Chapter 4: Traffic Stops
  • Barking Up the Wrong Tree?
  • The scientific process
  • Open data
  • Hack-4-Good
  • FsLab and type providers
  • Data exploration
  • Visualization
  • Decision trees
  • Accord
  • numl
  • Summary
  • Chapter 5: Time Out
  • Obtaining Data.
  • Overview
  • SQL Server providers
  • Non-type provider
  • SqlProvider
  • Deedle
  • MicrosoftSqlProvider
  • SQL Server type provider wrap up
  • Non SQL type providers
  • Combining data
  • Parallelism
  • JSON type provider
  • authentication
  • Summary
  • Chapter 6: AdventureWorks Redux
  • k-NN and Naïve Bayes Classifiers
  • k-Nearest Neighbors (k-NN)
  • k-NN example
  • Naïve Bayes
  • Naïve Bayes in action
  • One thing to keep in mind while using Naïve Bayes
  • AdventureWorks
  • Getting the data ready
  • k-NN and AdventureWorks data
  • Naïve Bayes and AdventureWorks data
  • Making use of our discoveries
  • Getting the data ready
  • Expanding features
  • Summary
  • Chapter 7: Traffic Stops and Crash Locations
  • When Two Datasets Are Better Than One
  • Unsupervised learning
  • k-means
  • Principle Component Analysis (PCA)
  • Traffic stop and crash exploration
  • Preparing the script and the data
  • Geolocation analysis
  • PCA
  • Analysis summary
  • The Code-4-Good application
  • Machine learning assembly
  • The UI
  • Adding distance calculations
  • Augmenting with human observations
  • Summary
  • Chapter 8: Feature Selection and Optimization
  • Cleaning data
  • Selecting data
  • Collinearity
  • Feature selection
  • Normalization
  • Scaling
  • Overfitting and cross validation
  • Cross validation
  • train versus test
  • Cross validation
  • the random and mean test
  • Cross validation
  • the confusion matrix and AUC
  • Cross validation
  • unrelated variables
  • Summary
  • Chapter 9: AdventureWorks Production
  • Neural Networks
  • Neural networks
  • Background
  • Neural network demo
  • Neural network
  • try #1
  • Neural network
  • try #2
  • Building the application
  • Setting up the models
  • Building the UX
  • Summary
  • Chapter 10: Big Data and IoT
  • AdventureWorks and the Internet of Bikes
  • Data considerations
  • MapReduce
  • MBrace.
  • Distributed logistic regression
  • The IoT
  • PCL linear regression
  • Service layer
  • Universal Windows app and Raspberry PI 2
  • Next steps
  • Summary
  • Index.

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