C# Machine Learning Projects (eBook, ePUB)
Nine real-world projects to build robust and high-performing machine learning models with C#
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Power 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 to grips with C# packages such as Accord.net, LiveCharts, and DeedleBook DescriptionMachine 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, an...
Power your C# and .NET applications with exciting machine learning models and modular projects
Key FeaturesProduce classification, regression, association, and clustering modelsExpand your understanding of machine learning and C# Get to grips with C# packages such as Accord.net, LiveCharts, and DeedleBook Description
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.
What you will learnSet up the C# environment for machine learning with required packagesBuild classification models for spam email filteringGet to grips with feature engineering using NLP techniques for Twitter sentiment analysisForecast foreign exchange rates using continuous and time-series dataMake a recommendation model for music genre recommendationFamiliarize yourself with munging image data and Neural Network models for handwritten-digit recognitionUse Principal Component Analysis (PCA) for cyber attack detectionOne-Class Support Vector Machine for credit card fraud detectionWho 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.
Yoon Hyup Hwang is a seasoned data scientist with several years of experience in marketing and finance industries with expertise in predictive modeling, machine learning, statistical analysis, and data engineering. He holds M.S.E. in Computer and Information Technology from the University of Pennsylvania, and a B.A. in Economics from the University of Chicago. In his free time, he enjoys training different martial arts (Krav Maga, BJJ, and Muay Thai), snowboarding, and roasting coffee.
Key FeaturesProduce classification, regression, association, and clustering modelsExpand your understanding of machine learning and C# Get to grips with C# packages such as Accord.net, LiveCharts, and DeedleBook Description
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.
What you will learnSet up the C# environment for machine learning with required packagesBuild classification models for spam email filteringGet to grips with feature engineering using NLP techniques for Twitter sentiment analysisForecast foreign exchange rates using continuous and time-series dataMake a recommendation model for music genre recommendationFamiliarize yourself with munging image data and Neural Network models for handwritten-digit recognitionUse Principal Component Analysis (PCA) for cyber attack detectionOne-Class Support Vector Machine for credit card fraud detectionWho 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.
Yoon Hyup Hwang is a seasoned data scientist with several years of experience in marketing and finance industries with expertise in predictive modeling, machine learning, statistical analysis, and data engineering. He holds M.S.E. in Computer and Information Technology from the University of Pennsylvania, and a B.A. in Economics from the University of Chicago. In his free time, he enjoys training different martial arts (Krav Maga, BJJ, and Muay Thai), snowboarding, and roasting coffee.
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