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

Format:

Book

Author/Creator:

Dixon, Jamie, author.

Series:

Community experience distilled.

Community experience distilled

Language:

English

Subjects (All):

Microsoft .NET Framework.

Physical Description:

1 online resource (358 p.)

Edition:

1st edition

Place of Publication:

Birmingham : Packt Publishing, 2016.

System Details:

text file

Biography/History:

Dixon Jamie: 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.

Summary:

Master the art of machine learning with .NET and gain insight into real-world applications About 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 .NET libraries for machine learning Implement several common machine learning techniques Evaluate, optimize and adjust machine learning models Who This Book Is For This 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 Learn Write your own machine learning applications and experiments using the latest .NET framework, including .NET Core 1.0 Set 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 Naïve 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 ?Big Data?, distributed computing, and how to deploy machine learning models to IoT devices ? making machines self-learning and adapting Along the way, learn about Open Data, Bing maps, and MBrace In 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...

Contents:

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

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed July 4, 2016).

ISBN:

9781785881190

1785881191

OCLC:

946526860