Mastering NET machine learning

www.allitebooks.com www.allitebooks.com Mastering NET Machine Learning www.allitebooks.com Table of Contents Mastering NET Machine Learning Credits About the Author Acknowledgments About the Reviewers www.PacktPub.com eBooks, discount offers, and more Why subscribe? Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Errata Piracy Questions 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# www.allitebooks.com Math.NET Accord.NET Numl Summary 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 RMSE Regression and the real world Regression against actual data AdventureWorks app Setting up the environment Updating the existing web project Implementing the regression Summary More AdventureWorks Regression Introduction to multiple linear regression Intro example Keep adding x variables? AdventureWorks data Adding multiple regression to our production application www.allitebooks.com 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 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 Time Out – Obtaining Data Overview SQL Server providers Non-type provider SqlProvider Deedle MicrosoftSqlProvider SQL Server type provider wrap up Non SQL type providers www.allitebooks.com Parallelism JSON type provider – authentication Summary 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 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 www.allitebooks.com Summary 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 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 10 Big Data and IoT AdventureWorks and the Internet of Bikes Data considerations MapReduce MBrace Distributed logistic regression www.allitebooks.com PCL linear regression Service layer Universal Windows app and Raspberry Pi 2 Next steps Summary Index www.allitebooks.com www.allitebooks.com I Internet of Bikes (IoB) and AdventureWorks / AdventureWorks and the Internet of Bikes overview / AdventureWorks and the Internet of Bikes data considerations / Data considerations MapReduce / MapReduce MBrace / MBrace distributed logistic regression / Distributed logistic regression Internet of Things (IoT) / Why write your own? IoT about / The IoT PCL linear regression / PCL linear regression service layer, building / Service layer Universal Windows app / Universal Windows app and Raspberry Pi 2 Raspberry PI 2 / Universal Windows app and Raspberry Pi 2 issues / Next steps J Java Virtual Machine (JVM) / What version of the NET Framework are we using? Join method parameters / Geolocation analysis JSON type provider about / JSON type provider – authentication K k-means / k-means k-NN about / k-Nearest Neighbors (k-NN) example / k-NN example and Adventure Works data / k-NN and AdventureWorks data L lambda expression / Learning F# Language Integrated Query (LINQ) / What version of the NET Framework are we using?, SqlProvider logistic regressions about / Intro to logistic regression URL / Intro to logistic regression x variable, adding / Adding another x variable applying, to AdventureWorks data / Applying a logistic regression to AdventureWorks data categorical data / Categorical data attachment point / Attachment point results, analyzing / Analyzing results of the logistic regression adding, to application / Adding logistic regression to the application low risk / Augmenting with human observations M machine learning (ML) about / What is machine learning?, Getting ready for machine learning implementing / Why write your own? Visual Studio, setting up / Setting up Visual Studio Math.NET URL / Math.NET about / Math.NET, Math.NET regression, calculating / Regression try 1, Regression try 2 MBrace URL / MBrace about / MBrace starter pack, URL / MBrace Mean Square Error (MSE) / Regression evaluation using RMSE multiple linear regression about / Introduction to multiple linear regression example / Intro example x variables, adding / Keep adding x variables? applying, to AdventureWorks data / AdventureWorks data adding, to production application / Adding multiple regression to our production application multiple x variables, considerations / Considerations when using multiple x variables third x variable, adding to model / Adding a third x variable to our model N NET advantages / Why NET? about / Why NET? NET Framework version / What version of the NET Framework are we using? URL / What version of the NET Framework are we using? Naïve Bayes about / Naïve Bayes using / Naïve Bayes in action using, consideration / One thing to keep in mind while using Naïve Bayes neural networks demo / Neural network demo testing / Neural network – try #1, Neural network – try #2 NOAA archives reference / JSON type provider – authentication numl about / numl Numl about / Numl URL / Numl O object relational mapping (ORM) / FsLab and type providers Open Data about / Open data open data about / Why open data? overfitting / Overfitting and cross validation P Pearson’s Correlation about / Preparing the test data, Pearson’s correlation URL / Pearson’s correlation Pearson’s Correlation about / Pearson’s correlation Portable Class Libraries (PCLs) / Non-type provider Portable Class Library (PCL) / What version of the NET Framework are we using? potentiometer URL / Universal Windows app and Raspberry Pi 2 Principle Component Analysis (PCA) about / Unsupervised learning, Principle Component Analysis (PCA) principle components, frame / Principle Component Analysis (PCA) ProductID / Overview production application multiple linear regression, adding / Adding multiple regression to our production application R Raspberry Pi URL / Next steps RSME used, for evaluating regression / Regression evaluation using RMSE S scientific process / The scientific process simple linear regression about / Simple linear regression environment, setting up / Setting up the environment test data, preparing / Preparing the test data standard deviation / Standard deviation Pearson’s Correlation / Pearson’s correlation performing / Linear regression SpeedModel class CurrentModel property / PCL linear regression Train method / PCL linear regression Classify method / PCL linear regression SQL Server providers about / SQL Server providers, SQL Server type provider wrap up non-type provider / Non-type provider SqlProvider / SqlProvider Deedle / Deedle MicrosoftSqlProvider / MicrosoftSqlProvider FSharp.Data.TypeProviders.SqlServerProvider / SQL Server type provider wrap up FSharp.Data.TypeProviders.EntityFrameworkProvider / SQL Server type provider wrap up FSharp.Data.SqlClient / SQL Server type provider wrap up FSharp.Data.SqlProvider / SQL Server type provider wrap up FSharp.EntityFramework.MicrosoftSqlServer / SQL Server type provider wrap up non SQL type providers / Non SQL type providers standard deviation URL / Standard deviation about / Standard deviation sum of squares error (SSE) / Regression evaluation using RMSE supervised learning / Unsupervised learning T Task Parallel Library (TPL) / What version of the NET Framework are we using? Test With Experiment block / The scientific process third-party libraries about / Third-party libraries Math.NET / Math.NET Accord.NET / Accord.NET Numl / Numl traffic stop and crash exploration about / Traffic stop and crash exploration script, preparing / Preparing the script and the data data, preparing / Preparing the script and the data geolocation analysis / Geolocation analysis PCA / PCA analysis summary / Analysis summary type providers about / FsLab and type providers URL / FsLab and type providers overview / Overview U Universal Windows Applications (UWA) / What version of the NET Framework are we using? unsupervised learning about / Unsupervised learning k-means / k-means Principle Component Analysis (PCA) / Principle Component Analysis (PCA) V Visual Studio setting up / Setting up Visual Studio URL / Setting up Visual Studio W Windows Communication Foundation (WCF) / What version of the NET Framework are we using? X x variable adding, to multiple linear regression / Keep adding x variables? ...www.allitebooks.com Mastering NET Machine Learning www.allitebooks.com Table of Contents Mastering NET Machine Learning Credits About the Author Acknowledgments... Universal Windows app and Raspberry Pi 2 Next steps Summary Index www.allitebooks.com www.allitebooks.com Mastering NET Machine Learning Mastering NET Machine Learning Copyright © 2016 Packt Publishing All rights reserved No part of this book may be reproduced, stored in a retrieval system,... 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