This is the code repository for C# Machine Learning Projects, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
Machine learning is applied in almost all kinds of real-world surroundings and industries, right from medicine to advertising; from finance to scientifc research. This book will help you learn how to choose a model for your problem, how to evaluate the performance of your models, and how you can use C# to build machine learning models for your future projects.
You will get an overview of the machine learning systems and how you, as a C# and .NET developer, can apply your existing knowledge to the wide gamut of intelligent applications, all through a project-based approach. You will start by setting up your C# environment for machine learning with the required packages, Accord.NET, LiveCharts, and Deedle. We will then take you right from building classifcation models for spam email fltering and applying NLP techniques to Twitter sentiment analysis, to time-series and regression analysis for forecasting foreign exchange rates and house prices, as well as drawing insights on customer segments in e-commerce. You will then build a recommendation model for music genre recommendation and an image recognition model for handwritten digits. Lastly, you will learn how to detect anomalies in network and credit card transaction data for cyber attack and credit card fraud detections.
By the end of this book, you will be putting your skills in practice and implementing your machine learning knowledge in real projects.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
The code will look like the following:
var barChart = DataBarBox.Show(
new string[] { "Ham", "Spam" },
new double[] {
hamEmailCount,
spamEmailCount
}
);
barChart.SetTitle("Ham vs. Spam in Sample Set");
To get the most out of this book, I recommend that you thoroughly follow each of the steps laid out in each chapter. Following through the code samples and running them on your own will help you understand better and get more comfortable with building ML models faster. I also recommend that you be adventurous and mix up the techniques and learning algorithms discussed in different chapters. When you are done with this book, it will be even better if you go through the projects yourself again from the beginning and start building your own versions of ML models for the individual projects.
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