Mastering .NET Machine Learning: Use machine learning in your .NET applications

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Key benefits

•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

Description

.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 fly.

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

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Amazon verified reviews

Rick Pack Sep 10, 2016

I often return to this book's discussion of cross validation in chapter 8 because the conversational tone, exercises, and topical breadth were didactically effective. This is an example of the book's especially helpful data science coverage, even for those like myself who are not, or only occasionally, developing in .NET.I primarily program in R and enjoyed learning about F#, a functional language like R that is open source and "runs great on Windows, Linux, and OS X". Open data uses feature prominently in "Mastering .NET Machine Learning", propelling the reader into an exciting world of free data opportunities for learning and social good. One needs no background with F#, .NET, or Visual Studio to progress through and enjoy this book. Jamie pleasantly discusses the basics, starting with installation, and empowers the reader with smart progressions. He takes the time to lead the reader through hands-on exercises that sometimes, particularly in the early sections (e.g., linear regression), include working through the underlying mathematics in a way just deep enough to encourage understanding without risking frustration.I suggest peering at the table of contents to appreciate the diverse topics and analytical techniques covered such as using k-means clustering and principal components analysis to identify high-crash areas in Chapter 7's "Code-4-Good" application and the aforementioned linear regression coverage. The occasional typo or omission (e.g., TDD is wrongly referenced in Chapter 7 as something that will be touched upon in Chapter 8) does not detract from the value of this one-of-a-kind data-science / web development / functional programming book.Jamie is among the most engaging speakers in Raleigh / Durham (RTP), North Carolina and his signature charisma occasionally comes through the writing, such as in his ambitious remark that he will "attempt to go through [the] entire book without using an if...then statement" by instead using pattern matching. Thank you for dedicating so much energy and care to this work.

Amazon Verified review

Mauro Jun 03, 2019

Muy bueno

Fredrick Aug 09, 2017

Really good, explains it even for beginners and allows you to dive into the area of ML

theHatter May 30, 2016

I want to preface this review with 2 key items.1. I spend a fair amount of time educating customers about machine learning.2. I spend a lot of time in machine learning and intelligence.This book is perhaps one of the best books a .net developer as well as a beginner could use for getting an introduction to machine learning. There are a few key points and reasons here. The first is that the book starts all the way from installing the tools and performing all of the basic coding that will be needed later in the book. This fundamentals in the first portion is something that many other books I have purchased did not cover. The book actually exposed me to a few new libraries that I found personally useful including a hands on approach with numl and accord.net, which are .net machine learning frameworks.I think the single most value-able aspect of this book is the practical approach that it takes. The book is very focused on building but also delivering machine learning solutions. This is the first book I have encountered that surfaces machine learning models all the way through modern web frameworks integrating with modern systems that you find in every day development environments. By the end of this book, I had hands on experience building and delivering a neural network and had even touched on dealing with big data from an IoT perspective. The final chapter is a connected bicycle.Finally, there is a hands on book for .Net that takes you from absolute beginner to able to deliver intelligent solutions to production. I will certainly be using this book as a way to up skill customers and the local community.

Dhruv Sakalley Jan 24, 2017

Top take-away from this book:If you are a .Net developer, you will get up and running practicing machine learning. This book is a very practical approach which will appeal to many .Net stack developers.

Mastering .NET Machine Learning: Use machine learning in your .NET applications

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