C# Machine Learning Projects: 9 real-world projects to build robust and high-performing machine learning models with C#

C# Machine Learning Projects: 9 real-world projects to build robust and high-performing machine learning models with C#

Power up your C# and .NET applications with exciting machine learning models and modular projectsKey FeaturesProduce classification, regression, association and clustering modelsExpand your understanding of machine learning and C#Get the grips of C# packages such as Accord.net, LiveCharts, DeedleBook DescriptionMachine 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 learnSet up C# environment for machine learning with required packagesEssential steps to build classification models for spam email filteringFeature engineering using NLP techniques for Twitter sentiment analysisForecast foreign exchange rates using continuous and time-series dataBuilding a recommendation model for Music Genre RecommendationData munging image dataset for handwritten digit recognitionChoosing the right confidence threshold for cyber attack detectionOne-Class Support Vector Machine for credit card fraud detectionWho This Book Is ForThis 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.About the AuthorYoon Hyup Hwang is a seasoned data scientist in marketing and finance industries with expertise in predictive modeling, machine learning, statistical analysis, and data engineering. In the past several years, some of the machine learning models he has built include personalized marketing for various media and e-commerce clients, improving email open/click rates and customer retention rates, anti-money laundering and fraud detection, predicting the likelihoods of loan approval and default, and predicting foreign exchange rates. He holds M.S.E. in Computer and Information Technology from University of Pennsylvania and B.A. in Economics from the University of Chicago.In his free time, Yoon enjoys training Krav Maga and Brazilian Jiu Jitsu, snowboarding, and roasting coffee. He currently works in New York and lives in New Jersey with his artist wife, Sunyoung, and a playful dog, Dali (named after Salvador Dali).

Author: Yoon Hyup Hwang

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