Mastering .NET machine learning : master the art of machine learning with .NET and gain insight into real-world applications /

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Dixon, Jamie (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing, 2016.
Colección:Community experience distilled.
Temas:
Microsoft .NET Framework.
Machine learning.
Apprentissage automatique.
COMPUTERS / General
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
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 CorrelationLinear 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 modelLogistic 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
  • SqlProviderDeedle; 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 OneUnsupervised 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

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