C# Machine Learning Projects pdf.pdf

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Title Page

C# Machine Learning Projects

Packt Upsell

Why subscribe?

PacktPub.com

Contributors

About the author

About the reviewer

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

Basics of Machine Learning Modeling

Key ML tasks and applications

Steps in building ML models

Setting up a C# environment for ML

Setting up Visual Studio for C#

Installing Accord.NET

Installing Deedle

Summary

Spam Email Filtering

Problem definition for the spam email filtering project

Data preparation

Email data analysis

Feature engineering for email data

Logistic regression versus Naive Bayes for email spam filtering

Classification model validations

Summary

Twitter Sentiment Analysis

Setting up the environment

Problem definition for Twitter sentiment analysis

Data preparation using Stanford CoreNLP

Data analysis using lemmas as tokens

Feature engineering using lemmatization and emoticons

Naive Bayes versus random forest

Model validations – ROC curve and AUC

Summary

Foreign Exchange Rate Forecast

Problem definition

Data preparation

Time series data analysis

Feature engineering

Moving average

Bollinger Bands

Lagged variables

Linear regression versus SVM

Model validations

Summary

Fair Value of House and Property

Problem definition

Categorical versus continuous variables

Non-ordinal categorical variables

Ordinal categorical variable

Continuous variable

Target variable – sale price

Feature engineering and encoding

Dummy variables

Feature encoding

Linear regression versus SVM with kernels

Linear regression

Linear SVM

SVM with a polynomial kernel

SVM with a Gaussian kernel

Model validations

Summary

Customer Segmentation

Problem definition

Data analysis for the online retail dataset

Handling missing values

Variable distributions

Feature engineering and data aggregation

Unsupervised learning – k-means clustering

Clustering model validations using the Silhouette Coefficient

Summary

Music Genre Recommendation

Problem definition

Data analysis for the audio features dataset

Target variable distribution

Audio features – MFCC

ML models for music genre classification

Logistic regression

SVM with the Gaussian kernel

Naive Bayes

Ensembling base learning models

Evaluating recommendation/rank-ordering models

Prediction accuracy

Confusion matrices

Mean Reciprocal Rank

Summary

Handwritten Digit Recognition

Problem definition

Data analysis for the image dataset

Target variable distribution

Handwritten digit images

Image features - pixels

Feature engineering and dimensionality reduction

Splitting the sample set into train versus test sets

Dimensionality reduction by PCA

ML models for handwritten digit recognition

Loading data

Logistic regression classifier

Naive Bayes classifier

Neural network classifier

Evaluating multi-class classification models

Confusion matrices

Accuracy and precision/recall

One versus Rest AUC

Summary

Cyber Attack Detection

Problem definition

Data analysis for internet traffic data

Data clean-up

Target variable distribution

Categorical variable distribution

Continuous variable distribution

Feature engineering and PCA

Target and categorical variables encoding

Fitting PCA

PCA features

Principal component classifier for anomaly detection

Preparation for training

Building a principal component classifier

Evaluating anomaly detection models

Summary

Credit Card Fraud Detection

Problem definition

Data analysis for anonymized credit card data

Target variable distribution

Feature distributions

Feature engineering and PCA

Preparation for feature engineering

Fitting a PCA

One-class SVM versus PCC

Preparation for model training

Principal component classifier

One-class SVM

Evaluating anomaly detection models

Principal Component Classifier

One-class SVM

Summary

What's Next?

Review

Steps for building ML models

Classification models

Regression models

Clustering algorithms

Real-life challenges

Data issues

Infrastructure issues

Explainability versus accuracy

Other common technologies

Other ML libraries

Data visualization libraries and tools

Technologies for data processing

Summary

Other Books You May Enjoy

Leave a review - let other readers know what you think

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

Yoon Hyup Hwang BIRMINGHAM - MUMBAI

C# Machine Learning Projects Copyright © 2018 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. Commissioning Editor: Pravin Dhandre Acquisition Editor: Tushar Gupta Content Development Editor: Aaryaman Singh Technical Editor: Sayli Nikalje Copy Editors: Safis Editing Project Coordinator: Manthan Patel Proofreader: Safis Editing Indexer: Tejal Daruwale Soni Graphics: Tania Dutta Production Coordinator: Arvindkumar Gupta First published: June 2018 Production reference: 1130618 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78899-640-2 www.packtpub.com

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