C# Machine Learning Projects

C# Machine Learning Projects: Nine real-world projects to build robust and high-performing machine learning models with C#C# Machine Learning Projects: Nine real-world projects to build robust and high-performing machine learning models with C#
by: Yoon Hyup Hwang
ISBN-10: 1788996402
ISBN-13: 9781788996402
Publisher finelybook 出版社: Packt Publishing (June 18,2018)
Print Length 页数: 350


Book Description
By finelybook

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.
Contents
1: BASICS OF MACHINE LEARNING MODELING
2: SPAM EMAIL FILTERING
3: TWITTER SENTIMENT ANALYSIS
4: FOREIGN EXCHANGE RATE FORECAST
5: FAIR VALUE OF HOUSE AND PROPERTY
6: CUSTOMER SEGMENTATION
7: MUSIC GENRE RECOMMENDATION
8: HANDWRITTEN DIGIT RECOGNITION
9: CYBER ATTACK DETECTION
10: CREDIT CARD FRAUD DETECTION
11: WHAT’S NEXT?
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
Authors
Yoon Hyup Hwang
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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