Index
A
- Accord / Accord
- Accord.NET
- about / Accord.NET, Accord.NET
- URL / Accord.NET
- regression / Regression
- regression evaluation, RSME used / Regression evaluation using RMSE
- regression, using with real world / Regression and the real world
- regression, against actual data / Regression against actual data
- AdventureWorks
- and Internet of Bikes (IoB) / AdventureWorks and the Internet of Bikes
- Adventure Works / Deedle
- URL / Regression against actual data
- about / AdventureWorks
- data, making available / Getting the data ready
- and k-NN / k-NN and AdventureWorks data
- and Naïve Bayes / Naïve Bayes and AdventureWorks data
- Adventure Works app
- about / AdventureWorks app
- environment, setting up / Setting up the environment
- existing web project, updating / Updating the existing web project
- regression, implementing / Implementing the regression
- AdventureWorks data
- multiple linear regression, applying / AdventureWorks data
- logistic regression, applying / Applying a logistic regression to AdventureWorks data
- Adventure Works data
- and k-NN / k-NN and AdventureWorks data
- Adventure Works database / SqlProvider
- application
- logistic regression, adding / Adding logistic regression to the application
- building / Building the application
- models, setting up / Setting up the models
- UX, building / Building the UX
- attachment point, logistic regressions / Attachment point
- Azure IoT suite
- URL / Next steps
B
- Bing Map API
- URL / Combining data
C
- classification models discoveries
- using / Making use of our discoveries
- data, making available / Getting the data ready
- features, expanding / Expanding features
- clean data block / The scientific process
- Code-4-Good application
- about / The Code-4-Good application
- machine learning assembly / Machine learning assembly
- UI / The UI
- distance calculations, adding / Adding distance calculations
- human observations, augmenting with / Augmenting with human observations
- Code for America
- URL / Open data
- collinearity / Collinearity
- cross validation
- about / Overfitting and cross validation
- training, versus testing / Cross validation – train versus test
- random test / Cross validation – the random and mean test
- mean test / Cross validation – the random and mean test
- confusion matrix / Cross validation – the confusion matrix and AUC
- AUC / Cross validation – the confusion matrix and AUC
- unrelated variables / Cross validation – unrelated variables
D
- data
- cleaning / Cleaning data
- selecting / Selecting data
- data combinations
- about / Combining data
- geolocation data / Combining data
- parallelism / Parallelism
- JSON type provider / JSON type provider – authentication
- data elements
- reference / Non SQL type providers
- data frame
- about / Overview
- data lakes / The scientific process
- data munging / The scientific process
- data selection
- about / Selecting data
- collinearity / Collinearity
- normalization / Normalization
- scaling / Scaling
- decision trees
- about / Decision trees
- benefits / Decision trees
- Accord / Accord
- numl / numl
- Deedle / Deedle
E
- Entity Framework (EF) / FsLab and type providers
F
- F#
- about / Why F#?
- features / Learning F#
- learning / Learning F#
- URLs / Learning F#
- Fiddler
- URL / Parallelism
- Framework Class Library (FCL) / What version of the .NET Framework are we using?
- FsLab
- reference / FsLab and type providers
G
- GroupJoin method / Geolocation analysis
H
- Hack-4-Good
- about / Hack-4-Good
- FsLab / FsLab and type providers
- type providers / FsLab and type providers
- data exploration / Data exploration
- visualization / Visualization
- high risk / Augmenting with human observations
- Highway 46
- URL / Data considerations
I
- Internet of Bikes (IoB)
- and AdventureWorks / AdventureWorks and the Internet of Bikes
- overview / AdventureWorks and the Internet of Bikes
- data considerations / Data considerations
- MapReduce / MapReduce
- MBrace / MBrace
- distributed logistic regression / Distributed logistic regression
- Internet of Things (IoT) / Why write your own?
- IoT
- about / The IoT
- PCL linear regression / PCL linear regression
- service layer, building / Service layer
- Universal Windows app / Universal Windows app and Raspberry Pi 2
- Raspberry PI 2 / Universal Windows app and Raspberry Pi 2
- issues / Next steps
J
- Java Virtual Machine (JVM) / What version of the .NET Framework are we using?
- Join method
- parameters / Geolocation analysis
- JSON type provider
K
- k-means / k-means
- k-NN
- about / k-Nearest Neighbors (k-NN)
- example / k-NN example
- and Adventure Works data / k-NN and AdventureWorks data
L
- lambda expression / Learning F#
- Language Integrated Query (LINQ) / What version of the .NET Framework are we using?, SqlProvider
- logistic regressions
- about / Intro to logistic regression
- URL / Intro to logistic regression
- x variable, adding / Adding another x variable
- applying, to AdventureWorks data / Applying a logistic regression to AdventureWorks data
- categorical data / Categorical data
- attachment point / Attachment point
- results, analyzing / Analyzing results of the logistic regression
- adding, to application / Adding logistic regression to the application
- low risk / Augmenting with human observations
M
- machine learning (ML)
- about / What is machine learning?, Getting ready for machine learning
- implementing / Why write your own?
- Visual Studio, setting up / Setting up Visual Studio
- Math.NET
- URL / Math.NET
- about / Math.NET, Math.NET
- regression, calculating / Regression try 1, Regression try 2
- MBrace
- Mean Square Error (MSE) / Regression evaluation using RMSE
- multiple linear regression
- about / Introduction to multiple linear regression
- example / Intro example
- x variables, adding / Keep adding x variables?
- applying, to AdventureWorks data / AdventureWorks data
- adding, to production application / Adding multiple regression to our production application
- multiple x variables, considerations / Considerations when using multiple x variables
- third x variable, adding to model / Adding a third x variable to our model
N
- .NET
- .NET Framework
- Naïve Bayes
- about / Naïve Bayes
- using / Naïve Bayes in action
- using, consideration / One thing to keep in mind while using Naïve Bayes
- neural networks
- demo / Neural network demo
- testing / Neural network – try #1, Neural network – try #2
- NOAA archives
- reference / JSON type provider – authentication
- numl
- about / numl
- Numl
O
- object relational mapping (ORM) / FsLab and type providers
- Open Data
- about / Open data
- open data
- about / Why open data?
- overfitting / Overfitting and cross validation
P
- Pearsons Correlation
- Pearson’s Correlation
- about / Pearson's correlation
- Portable Class Libraries (PCLs) / Non-type provider
- Portable Class Library (PCL) / What version of the .NET Framework are we using?
- potentiometer
- Principle Component Analysis (PCA)
- principle components, frame / Principle Component Analysis (PCA)
- ProductID / Overview
- production application
- multiple linear regression, adding / Adding multiple regression to our production application
R
- Raspberry Pi
- URL / Next steps
- RSME
- used, for evaluating regression / Regression evaluation using RMSE
S
- scientific process / The scientific process
- simple linear regression
- about / Simple linear regression
- environment, setting up / Setting up the environment
- test data, preparing / Preparing the test data
- standard deviation / Standard deviation
- Pearsons Correlation / Pearson's correlation
- performing / Linear regression
- SpeedModel class
- CurrentModel property / PCL linear regression
- Train method / PCL linear regression
- Classify method / PCL linear regression
- SQL Server providers
- about / SQL Server providers, SQL Server type provider wrap up
- non-type provider / Non-type provider
- SqlProvider / SqlProvider
- Deedle / Deedle
- MicrosoftSqlProvider / MicrosoftSqlProvider
- FSharp.Data.TypeProviders.SqlServerProvider / SQL Server type provider wrap up
- FSharp.Data.TypeProviders.EntityFrameworkProvider / SQL Server type provider wrap up
- FSharp.Data.SqlClient / SQL Server type provider wrap up
- FSharp.Data.SqlProvider / SQL Server type provider wrap up
- FSharp.EntityFramework.MicrosoftSqlServer / SQL Server type provider wrap up
- non SQL type providers / Non SQL type providers
- standard deviation
- URL / Standard deviation
- about / Standard deviation
- sum of squares error (SSE) / Regression evaluation using RMSE
- supervised learning / Unsupervised learning
T
- Task Parallel Library (TPL) / What version of the .NET Framework are we using?
- Test With Experiment block / The scientific process
- third-party libraries
- about / Third-party libraries
- Math.NET / Math.NET
- Accord.NET / Accord.NET
- Numl / Numl
- traffic stop and crash exploration
- about / Traffic stop and crash exploration
- script, preparing / Preparing the script and the data
- data, preparing / Preparing the script and the data
- geolocation analysis / Geolocation analysis
- PCA / PCA
- analysis summary / Analysis summary
- type providers
- about / FsLab and type providers
- URL / FsLab and type providers
- overview / Overview
U
- Universal Windows Applications (UWA) / What version of the .NET Framework are we using?
- unsupervised learning
- about / Unsupervised learning
- k-means / k-means
- Principle Component Analysis (PCA) / Principle Component Analysis (PCA)
V
- Visual Studio
- setting up / Setting up Visual Studio
- URL / Setting up Visual Studio
W
- Windows Communication Foundation (WCF) / What version of the .NET Framework are we using?
X
- x variable
- adding, to multiple linear regression / Keep adding x variables?