Power your C# and .NET applications with exciting machine learning models and modular projects
About This Book
* Produce classification, regression, association, and clustering models
* Expand your understanding of machine learning and C#
* Get to grips with C# packages such as Accord.net, LiveCharts, and Deedle
Who This Book Is For
If you're a C# or .NET developer with good knowledge of C#, then this book is perfect for you to get Machine Learning into your projects and make smarter applications.
What You Will Learn
* Set up the C# environment for machine learning with required packages
* Build classification models for spam email filtering
* Get to grips with feature engineering using NLP techniques for Twitter sentiment analysis
* Forecast foreign exchange rates using continuous and time-series data
* Make a recommendation model for music genre recommendation
* Familiarize yourself with munging image data and Neural Network models for handwritten-digit recognition
* Use Principal Component Analysis (PCA) for cyber attack detection
* One-Class Support Vector Machine for credit card fraud detection
In Detail
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.
Style and approach
A unique, learn-as-you-do approach, as the reader builds on his understanding of C# progressively with each project. This book is designed in such a way that implementing each project will empower you with a unique skillset, and enable you to implement the next project more confidently.
About This Book
* Produce classification, regression, association, and clustering models
* Expand your understanding of machine learning and C#
* Get to grips with C# packages such as Accord.net, LiveCharts, and Deedle
Who This Book Is For
If you're a C# or .NET developer with good knowledge of C#, then this book is perfect for you to get Machine Learning into your projects and make smarter applications.
What You Will Learn
* Set up the C# environment for machine learning with required packages
* Build classification models for spam email filtering
* Get to grips with feature engineering using NLP techniques for Twitter sentiment analysis
* Forecast foreign exchange rates using continuous and time-series data
* Make a recommendation model for music genre recommendation
* Familiarize yourself with munging image data and Neural Network models for handwritten-digit recognition
* Use Principal Component Analysis (PCA) for cyber attack detection
* One-Class Support Vector Machine for credit card fraud detection
In Detail
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.
Style and approach
A unique, learn-as-you-do approach, as the reader builds on his understanding of C# progressively with each project. This book is designed in such a way that implementing each project will empower you with a unique skillset, and enable you to implement the next project more confidently.
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