- eBook:C# Machine Learning Projects: Nine real-world projects to build robust and high-performing machine learning models with C#
- Author:Yoon Hyup Hwang
- Data:June 18, 2018
- Pages:350 pages
- Format:PDF, ePUB
- Develop classification, regression, association, and clustering models
- Expand your understanding of machine learning and C#
- Get to grips with C# packages such as Accord.NET, Live Charts, and Deedle
Book DescriptionMachine learning is applied in a variety of real-world surroundings and industries, right from medicine, to advertising, and even finance. This book will help you learn to choose a model for your problem, evaluate the performance of your models, and use C# to build machine learning models for future projects. With this guide, you'll explore machine learning systems and how you can apply your existing knowledge to the wide range of intelligent applications through a project-based approach.
You will start by setting up your C# environment for machine learning with packages, such as Accord.NET and Deedle. Next, the book will guide you through building classification models for spam email filtering, applying natural language processing (NLP) techniques for Twitter Sentiment Analysis, and even understanding time series and regression analysis for forecasting foreign exchange rates and house prices. You'll also be able to derive insights from customer segments in e-commerce. Later, you'll get to grips with building a recommendation model for music genre recommendation and an image recognition model for handwritten digits. Finally, you'll learn to detect anomalies in network and credit card transaction data for cyber attack and credit card fraud detection.
By the end of this book, you'll be equipped with the skills to implement your machine learning knowledge in real-world projects.
What you will learn
- Set up the C# environment for machine learning with the 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
- Apply Principal Component Analysis (PCA) for cyber attack detection
- UseOne-Class Support Vector Machine for credit card fraud detection
Who This Book Is ForYou will find this book useful if you're a C# or .NET developer with good knowledge of C# who wants to implement machine learning in your projects and make smarter applications.
Chapter 2: Spam Email Filtering
Chapter 3: Twitter Sentiment Analysis
Chapter 4: Foreign Exchange Rate Forecast
Chapter 5: Fair Value of House and Property
Chapter 6: Customer Segmentation
Chapter 7: Music Genre Recommendation
Chapter 8: Handwritten Digit Recognition
Chapter 9: Cyber Attack Detection
Chapter 10: Credit Card Fraud Detection
Chapter 11: What's Next?