发帖
雷达卡
Credits
About the Author
Acknowledgments
About the Reviewers
eBooks, discount offers, and more
Why subscribe?
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
1. Welcome to Machine Learning Using the .NET Framework
What is machine learning?
Why .NET?
What version of the .NET Framework are we using?
Why write your own?
Why open data?
Why F#?
Getting ready for machine learning
Setting up Visual Studio
Learning F#
Third-party libraries
Math.NET
Accord.NET
Summary
2. AdventureWorks Regression
Simple linear regression
Setting up the environment
Preparing the test data
Standard deviation
Pearson’s correlation
Linear regression
Math.NET
Regression try 1
Regression try 2
Accord.NET
Regression
Regression evaluation using RMSE
Regression and the real world
Regression against actual data
AdventureWorks app
Setting up the environment
Updating the existing web project
Implementing the regression
Summary
3. More AdventureWorks Regression
Introduction to multiple linear regression
Intro example
Keep adding x variables?
AdventureWorks data
Adding multiple regression to our production application
Considerations when using multiple x variables
Adding a third x variable to our model
Logistic regression
Intro to logistic regression
Adding another x variable
Applying a logistic regression to AdventureWorks data
Categorical data
Attachment point
Analyzing results of the logistic regression
Adding logistic regression to the application
Summary
4. Traffic Stops – Barking Up the Wrong Tree?
The scientific process
Open data
Hack-4-Good
FsLab and type providers
Data exploration
Visualization
Decision trees
Accord
numl
Summary
5. Time Out – Obtaining Data
Overview
SQL Server providers
Non-type provider
SqlProvider
Deedle
MicrosoftSqlProvider
SQL Server type provider wrap up
Non SQL type providers
Combining data
Parallelism
JSON type provider – authentication
Summary
6. AdventureWorks Redux – k-NN and Naïve Bayes Classifiers
k-Nearest Neighbors (k-NN)
k-NN example
Naïve Bayes
Naïve Bayes in action
One thing to keep in mind while using Naïve Bayes
AdventureWorks
Getting the data ready
k-NN and AdventureWorks data
Naïve Bayes and AdventureWorks data
Making use of our discoveries
Getting the data ready
Expanding features
Summary
7. Traffic Stops and Crash Locations – When Two Datasets Are Better Than One
Unsupervised learning
k-means
Principle Component Analysis (PCA)
Traffic stop and crash exploration
Preparing the script and the data
Geolocation analysis
PCA
Analysis summary
The Code-4-Good application
Machine learning assembly
The UI
Adding distance calculations
Augmenting with human observations
Summary
8. Feature Selection and Optimization
Cleaning data
Selecting data
Collinearity
Feature selection
Normalization
Scaling
Overfitting and cross validation
Cross validation – train versus test
Cross validation – the random and mean test
Cross validation – the confusion matrix and AUC
Cross validation – unrelated variables
Summary
9. AdventureWorks Production – Neural Networks
Neural networks
Background
Neural network demo
Neural network – try #1
Neural network – try #2
Building the application
Setting up the models
Building the UX
Summary
10. Big Data and IoT
AdventureWorks and the Internet of Bikes
Data considerations
MapReduce
MBrace
Distributed logistic regression
The IoT
PCL linear regression
Service layer
Universal Windows app and Raspberry Pi 2
Next steps
Summary
Index
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