Mastering .NET Machine Learning: Master the art of machine learning with .NET and gain insight into real-world applications

Key Features

• Based on .NET framework 4.6.1, includes examples on ASP .NET 5.0
• 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

Book 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

• 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 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 "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

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 data sets and merging software engineering techniques to scientific computing. When he codes for fun, he spends his time using the .NET Micro framework with Netduinos, Raspberry Pi2s, and the Kinect.

Jamie has a BSCS 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 for Open Data. He also is involved with his local .NET User Group (TRINUG) with an emphasis on data analytics, machine learning, and the internet of things (IoT).

Jamie's most recent failures is getting voted off of tech-ed idol by saying that end users are stupid, destroying several hundred dollars of robotic equipment in an ill-advised experiment involving lawnmowers, and finishing in the bottom 25% of a high-stakes Kaggle competition (to be fair, he only had 2 hours to do it…).

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 @jamie_dixon.