Book Description
Machine Learning is applied in almost all kinds of real-world surroundings and industries, right from medicine to advertising, finance and scientific research. This book 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 C#, Net users can apply your existing knowledge to the wide gamut of intelligent applications through a project-based approach. You will start by setting up your C# environment for machine learning with required packages, Accord.NET, LiveCharts, Deedle. We will then take you right from classification models for spam email filtering, NLP techniques for Twitter sentiment analysis, time-series data for forecasting foreign exchange rates to drawing insights from Customer segmentation in E-commerce. You will then build a recommendation model for Music Genre Recommendation, followed by, Munging data from image dataset for handwritten digit recognition. Lastly, you will learn to detect Anamoly in cyber attack & credit card fraud detection.
By the end of this book, you will be putting your skills in practice and running your machine learning knowledge in implementing real projects using this project-based book.
What you will learn
- Set up C# environment for machine learning with required packages
- Essential steps to build classification models for spam email filtering
- Feature engineering using NLP techniques for Twitter sentiment analysis
- Forecast foreign exchange rates using continuous and time-series data
- Building a recommendation model for Music Genre Recommendation
- Data munging image dataset for handwritten digit recognition
- Choosing the right confidence threshold for cyber attack detection
- One-Class Support Vector Machine for credit card fraud detection
Who This Book Is For
This book is for C# & .NET developers who have strong knowledge of C#, then this book is perfect for you to get machine learning into your real-world projects and make the application much smarter.
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