Mastering .NET Machine Learning

 
 
Packt Publishing Limited
  • 1. Auflage
  • |
  • erschienen am 29. März 2016
  • |
  • 358 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-78588-119-0 (ISBN)
 
Master the art of machine learning with .NET and gain insight into real-world applicationsAbout This BookBased on .NET framework 4.6.1, includes examples on ASP.NET Core 1.0Set up your business application to start using machine learning techniquesFamiliarize the user with some of the more common .NET libraries for machine learningImplement several common machine learning techniquesEvaluate, optimize and adjust machine learning modelsWho This Book Is ForThis book is targeted at .Net developers who want to build complex machine learning systems. Some basic understanding of data science is required.What You Will LearnWrite your own machine learning applications and experiments using the latest .NET framework, including .NET Core 1.0Set up your business application to start using machine learning.Accurately predict the future using regressions.Discover hidden patterns using decision trees.Acquire, prepare, and combine datasets to drive insights.Optimize business throughput using Bayes Classifier.Discover (more) hidden patterns using KNN and Naive Bayes.Discover (even more) hidden patterns using K-Means and PCA.Use Neural Networks to improve business decision making while using the latest ASP.NET technologies.Explore &quote;Big Data&quote;, distributed computing, and how to deploy machine learning models to IoT devices - making machines self-learning and adaptingAlong the way, learn about Open Data, Bing maps, and MBraceIn Detail.Net is one of the widely used platforms for developing applications. With the meteoric rise of Machine learning, developers are now keen on finding out how can they make their .Net applications smarter. Also, .NET developers are interested into moving into the world of devices and how to apply machine learning techniques to, well, machines.This book is packed with real-world examples to easily use machine learning techniques in your business applications. You will begin with introduction to F# and prepare yourselves for machine learning using .NET framework. You will be writing a simple linear regression model using an example which predicts sales of a product. Forming a base with the regression model, you will start using machine learning libraries available in .NET framework such as Math.NET, Numl.NET and Accord.NET with the help of a sample application. You will then move on to writing multiple linear regressions and logistic regressions.You will learn what is open data and the awesomeness of type providers. Next, you are going to address some of the issues that we have been glossing over so far and take a deep dive into obtaining, cleaning, and organizing our data. You will compare the utility of building a KNN and Naive Bayes model to achieve best possible results.Implementation of Kmeans and PCA using Accord.NET and Numl.NET libraries is covered with the help of an example application. We will then look at many of issues confronting creating real-world machine learning models like overfitting and how to combat them using confusion matrixes, scaling, normalization, and feature selection. You will now enter into the world of Neural Networks and move your line of business application to a hybrid scientific application. After you have covered all the above machine learning models, you will see how to deal with very large datasets using MBrace and how to deploy machine learning models to Internet of Thing (IoT) devices so that the machine can learn and adapt on the flyStyle and approach This book will guide you in learning everything about how to tackle the flood of data being encountered these days in your .NET applications with the help of popular machine learning libraries offered by the .NET framework.
  • Englisch
  • Birmingham
  • |
  • Großbritannien
978-1-78588-119-0 (9781785881190)
1785881191 (1785881191)
weitere Ausgaben werden ermittelt
Jamie Dixon has been writing code for as long as he can remember and has been getting paid to do it since 1995. He was using C# and JavaScript almost exclusively until discovering F#, and now combines all three languages for the problem at hand. He has a passion for discovering overlooked gems in datasets and merging software engineering techniques to scientific computing. When he codes for fun, he spends his time using Phidgets, Netduinos, and Raspberry Pis or spending time in Kaggle competitions using F# or R.
Jamie is a bachelor of science in computer science and has been an F# MVP since 2014. He is the former chair of his town's Information Services Advisory Board and is an outspoken advocate of open data. He is also involved with his local .NET User Group (TRINUG) with an emphasis on data analytics, machine learning, and the Internet of Things (IoT).
Jamie lives in Cary, North Carolina with his wonderful wife Jill and their three awesome children: Sonoma, Sawyer, and Sloan. He blogs weekly at jamessdixon. wordpress.com and can be found on Twitter at @jamie_dixon.
  • 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

Dateiformat: EPUB
Kopierschutz: Adobe-DRM (Digital Rights Management)

Systemvoraussetzungen:

Computer (Windows; MacOS X; Linux): Installieren Sie bereits vor dem Download die kostenlose Software Adobe Digital Editions (siehe E-Book Hilfe).

Tablet/Smartphone (Android; iOS): Installieren Sie bereits vor dem Download die kostenlose App Adobe Digital Editions (siehe E-Book Hilfe).

E-Book-Reader: Bookeen, Kobo, Pocketbook, Sony, Tolino u.v.a.m. (nicht Kindle)

Das Dateiformat EPUB ist sehr gut für Romane und Sachbücher geeignet - also für "fließenden" Text ohne komplexes Layout. Bei E-Readern oder Smartphones passt sich der Zeilen- und Seitenumbruch automatisch den kleinen Displays an. Mit Adobe-DRM wird hier ein "harter" Kopierschutz verwendet. Wenn die notwendigen Voraussetzungen nicht vorliegen, können Sie das E-Book leider nicht öffnen. Daher müssen Sie bereits vor dem Download Ihre Lese-Hardware vorbereiten.

Weitere Informationen finden Sie in unserer E-Book Hilfe.


Dateiformat: PDF
Kopierschutz: Adobe-DRM (Digital Rights Management)

Systemvoraussetzungen:

Computer (Windows; MacOS X; Linux): Installieren Sie bereits vor dem Download die kostenlose Software Adobe Digital Editions (siehe E-Book Hilfe).

Tablet/Smartphone (Android; iOS): Installieren Sie bereits vor dem Download die kostenlose App Adobe Digital Editions (siehe E-Book Hilfe).

E-Book-Reader: Bookeen, Kobo, Pocketbook, Sony, Tolino u.v.a.m. (nicht Kindle)

Das Dateiformat PDF zeigt auf jeder Hardware eine Buchseite stets identisch an. Daher ist eine PDF auch für ein komplexes Layout geeignet, wie es bei Lehr- und Fachbüchern verwendet wird (Bilder, Tabellen, Spalten, Fußnoten). Bei kleinen Displays von E-Readern oder Smartphones sind PDF leider eher nervig, weil zu viel Scrollen notwendig ist. Mit Adobe-DRM wird hier ein "harter" Kopierschutz verwendet. Wenn die notwendigen Voraussetzungen nicht vorliegen, können Sie das E-Book leider nicht öffnen. Daher müssen Sie bereits vor dem Download Ihre Lese-Hardware vorbereiten.

Weitere Informationen finden Sie in unserer E-Book Hilfe.


Download (sofort verfügbar)

43,65 €
inkl. 19% MwSt.
Download / Einzel-Lizenz
ePUB mit Adobe DRM
siehe Systemvoraussetzungen
PDF mit Adobe DRM
siehe Systemvoraussetzungen
Hinweis: Die Auswahl des von Ihnen gewünschten Dateiformats und des Kopierschutzes erfolgt erst im System des E-Book Anbieters
E-Book bestellen

Unsere Web-Seiten verwenden Cookies. Mit der Nutzung dieser Web-Seiten erklären Sie sich damit einverstanden. Mehr Informationen finden Sie in unserem Datenschutzhinweis. Ok