# Index

## A

- Accord.NET
- URL / Machine learning frameworks, Math.NET Numerics for F# 3.7.0
- about / Objective, Math.NET Numerics for F# 3.7.0

- accuracy metrics
- ranking / Ranking accuracy metrics

- accuracy parameters, for eecommendations evaluation
- about / Evaluating recommendations

- accuracy parameters, for recommendations evaluation
- prediction accuracy / Prediction accuracy
- confusion matrix / Confusion matrix (decision support)

- anomalies
- determining, with Chi-squared statistic / Chi-squared statistic to determine anomalies
- detecting, density estimation used / Detecting anomalies using density estimation

- Anomaly detection / Different areas where machine learning is being used
- anomaly detection
- actions / Some cool things you will do
- types / The different types of anomalies

- APIs
- Math.NET Numerics / Math.NET Numerics for F# 3.7.0

- asymmetric binary attributes similarity
- about / Similarity of asymmetric binary attributes
- Sokal-Sneath 1 index / Similarity of asymmetric binary attributes
- Sokal - Sneath 2 index / Similarity of asymmetric binary attributes
- Sokal - Sneath 3 index / Similarity of asymmetric binary attributes
- Sokal - Sneath 4 index / Similarity of asymmetric binary attributes
- Jaccard coefficient / Similarity of asymmetric binary attributes
- simple matching / Similarity of asymmetric binary attributes
- Tanimoto coefficient / Similarity of asymmetric binary attributes

- Atrial Premature Contraction / The different types of anomalies

## B

- bag of words (BoW) model / Different IR algorithms you will learn
- baseline predictors
- about / Baseline predictors
- code / Code walkthrough

- basic user-user collaborative filtering
- implementing, F# used / Implementing basic user-user collaborative filtering using F#

- binary classification
- k-NN, using / Binary classification using k-NN
- logistic regression, using / Binary classification using logistic regression (using Accord.NET)

## C

- Chi-squared statistic
- used, for determining anomalies / Chi-squared statistic to determine anomalies

- classification algorithms
- types / Different classification algorithms you will learn
- about / Different classification algorithms you will learn

- clustering / Different areas where machine learning is being used
- Cold Start
- about / Baseline predictors

- collaborative filtering
- about / Vocabulary of collaborative filtering
- User-User collaborative filtering / Vocabulary of collaborative filtering
- Item-Item collaborative filtering / Vocabulary of collaborative filtering

- Collaborative filtering / Recommender systems
- collective anomalies
- about / The different types of anomalies

- color images
- grouping / Grouping/clustering color images based on Canberra distance
- clustering / Grouping/clustering color images based on Canberra distance

- confusion matrix
- about / Confusion matrix (decision support)

- contextual anomalies
- about / The different types of anomalies
- contextual attributes / The different types of anomalies
- behavioral attributes / The different types of anomalies

- countBy / Generating a PDF from a histogram

## D

- decision tree
- used, for multiclass classification / Multiclass classification using decision trees
- working / How does it work?
- used, for predicting traffic jam / Predicting a traffic jam using a decision tree: a case study

- decision tree algorithm
- about / Decision tree algorithms
- linear regression / Linear regression
- logistic regression / Logistic regression
- recommender systems / Recommender systems

- Deedle
- URL / Why use F#?

- density estimation
- used, for detecting anomalies / Detecting anomalies using density estimation

- Dew point / Putting it together with Math.NET and FsPlot
- distance function / How does this work?
- distance metrics
- example usages / Some example usages of distance metrics

## E

- Ensemble method / Summary
- example usages, distance metrics
- about / Some example usages of distance metrics
- asymmetric binary similarity measures, using / Finding similar cookies using asymmetric binary similarity measures

## F

- F#
- about / Why use F#?
- benefits / Why use F#?
- type providers / Why use F#?
- supervised learning / Supervised machine learning
- used, for searching linear regression coefficients / Finding linear regression coefficients using F#
- used, for implementing basic user-user collaborative filtering / Implementing basic user-user collaborative filtering using F#

- F# 3.7.0
- Math.NET Numerics / Math.NET Numerics for F# 3.7.0

- F# wrapp / Getting Math.NET
- feature
- scaling / Feature scaling

- frameworks, machine learning
- Accord.NET / Machine learning frameworks
- WekaSharp / Machine learning frameworks

- FsPlot
- about / APIs used
- URL / APIs used
- used, for generating linear regression coefficients / Putting it together with Math.NET and FsPlot

## G

- gap calculations
- variations / Variations of gap calculations and similarity measures

- Grubb's test
- used, for detecting point anomalies / Detecting point anomalies using Grubb's test
- used, for transforming multivariate data / Grubb's test for multivariate data using Mahalanobis distance
- covariance matrix / Code walkthrough

## H

- handwritten digits
- recognizing / Recognizing handwritten digits – your "Hello World" ML program
- working / How does this work?

- HighCharts / Finding linear regression coefficients using F#
- histogram
- pdf, generating / Generating a PDF from a histogram

## I

- Inner Product family
- about / Inner Product family
- Inner-product distance / Inner Product family
- Harmonic distance / Inner Product family
- Cosine Similarity distance measure / Inner Product family
- Dice coefficient / Inner Product family

- Inter Quartile Range (IQR)
- used, for detecting point anomalies / Detecting point anomalies using IQR (Interquartile Range)
- about / Detecting point anomalies using IQR (Interquartile Range)

- Intersection family
- about / Intersection family
- Intersection distance / Intersection family
- Wave Hedges distance / Intersection family
- Czekanowski distance / Intersection family
- Ruzicka distance / Intersection family

- inverse document frequency (idf) / Information retrieval using tf-idf
- IR
- about / Objective
- algorithms / Different IR algorithms you will learn
- tf-idf, using / Information retrieval using tf-idf
- similarity measures / Measures of similarity

- IR algorithms
- distance based / Different IR algorithms you will learn
- set based / Different IR algorithms you will learn

- IR distance
- using / What interesting things can you do?

- iris flowers
- Iris-versicolor / Multiclass classification using logistic regression
- Iris-setosa / Multiclass classification using logistic regression
- Iris-virginica / Multiclass classification using logistic regression

- item-item collaborative filtering
- about / Item-item collaborative filtering

## K

- k-Nearest Neighbor (k-NN algorithm)
- about / Nearest Neighbour algorithm (a.k.a k-NN algorithm)
- reference, URLs / Nearest Neighbour algorithm (a.k.a k-NN algorithm)

- k-NN
- used, for binary classification / Binary classification using k-NN, How does it work?
- working / How does it work?
- used, for finding cancerous cells / Finding cancerous cells using k-NN: a case study

- Kaggle
- about / Machine learning for fun and profit
- URL / Recognizing handwritten digits – your "Hello World" ML program

## L

- L1 family
- about / L1 family
- Sørensen / L1 family
- Gower distance / L1 family
- Soergel / L1 family
- kulczynski d / L1 family
- kulczynski s / L1 family
- Canberra distance / L1 family

- least square
- linear regression method / Linear regression method of least square

- linear regression
- algorithms, types / Different types of linear regression algorithms
- APIs / APIs used

- linear regression coefficients
- searching, with F# / Finding linear regression coefficients using F#
- searching, with Math.NET / Finding the linear regression coefficients using Math.NET

- logistic regression
- about / Understanding logistic regression
- sigmoid function chart / The sigmoid function chart
- used, for binary classification / Binary classification using logistic regression (using Accord.NET)
- used, for multiclass classification / Multiclass classification using logistic regression

## M

- machine learning
- overview / Objective
- URL / Getting in touch
- using, areas / Different areas where machine learning is being used
- frameworks / Machine learning frameworks
- Kaggle / Machine learning for fun and profit
- using / Some interesting things you can do

- Mahalanobis distance
- Grubb's test,used for transforming multivariate data / Grubb's test for multivariate data using Mahalanobis distance

- Math.NET
- about / Objective
- used, for searching linear regression coefficients / Finding the linear regression coefficients using Math.NET
- used, for generating linear regression coefficients / Putting it together with Math.NET and FsPlot
- using, for multiple linear regression / Multiple linear regression and variations using Math.NET

- Math.NET Numerics
- about / Math.NET Numerics for F# 3.7.0
- obtaining / Getting Math.NET
- using / Experimenting with Math.NET

- matrix
- about / The basics of matrices and vectors (a short and sweet refresher)
- creating / Creating a matrix
- creating, by hand / Creating a matrix
- creating, from list of rows / Creating a matrix
- transpose, finding / Finding the transpose of a matrix
- inverse, finding / Finding the inverse of a matrix
- trace / Trace of a matrix
- QR decomposition / QR decomposition of a matrix
- Single Value Decomposition (SVD) / SVD of a matrix

- Minkowski distance
- about / Minkowski family
- Euclidean distance / Minkowski family
- City block distance / Minkowski family
- Chebyshev distance / Minkowski family

- Movie Lens 100K dataset
- reference link / Working with real movie review data (Movie Lens)

- movie ratings dataset, u.data file
- reference link / Working with real movie review data (Movie Lens)

- multiclass classification
- logistic regression, using / Multiclass classification using logistic regression
- working / How does it work?
- decision trees, using / Multiclass classification using decision trees
- WekaSharp, using / Obtaining and using WekaSharp
- WekaSharp, obtaining / Obtaining and using WekaSharp

- multiple linear regression
- about / Multiple linear regression
- Math.NET, using / Multiple linear regression and variations using Math.NET
- and variation / Multiple linear regression and variations using Math.NET
- result, plotting / Plotting the result of multiple linear regression

- multivariate data
- transforming, with Grubb's test / Grubb's test for multivariate data using Mahalanobis distance

- multivariate multiple linear regression
- about / Multivariate multiple linear regression

## N

- negations
- handling / Handling negations

- NuGet page
- API, URL / Math.NET Numerics for F# 3.7.0

## P

- pdf
- generating, from histogram / Generating a PDF from a histogram

- Pearson's correlation coefficient / Basis of User-User collaborative filtering
- point anomalies
- about / The different types of anomalies
- detecting, Inter Quartile Range (IQR) used / Detecting point anomalies using IQR (Interquartile Range)
- detecting, with Grubb's test / Detecting point anomalies using Grubb's test

- prediction-rating correlation
- about / Prediction-rating correlation

- probability distribution functions (pdf) / Measures of similarity

## R

- real movie review data (Movie Lens)
- working with / Working with real movie review data (Movie Lens)

- recommendations
- evaluating / Evaluating recommendations

- Recommender systems / Different areas where machine learning is being used
- Reinforcement Learning / Different areas where machine learning is being used
- Relative Humidity (RH) / Putting it together with Math.NET and FsPlot
- ridge regression
- about / Ridge regression

## S

- Semantic Orientation (SO)
- about / Identifying praise or criticism with sentiment orientation
- used, for identifying praise / Identifying praise or criticism with sentiment orientation, Pointwise Mutual Information
- used, for identifying criticism / Identifying praise or criticism with sentiment orientation, Pointwise Mutual Information

- Sentiment Analysis (SA)
- finding, SO-PMI used / Using SO-PMI to find sentiment analysis

- Sentiment Analysis algorithms
- about / A baseline algorithm for SA using SentiWordNet lexicons

- SentiWordNet
- download link / A baseline algorithm for SA using SentiWordNet lexicons

- SentiWordNet lexicons
- about / A baseline algorithm for SA using SentiWordNet lexicons

- set based similarity measures, Shannons Entropy family
- Jaccard index / Set-based similarity measures
- Tversky index / Set-based similarity measures

- Shannons Entropy family
- Kulback Leiblers distance measure / Shannon's Entropy family
- Jeffreys distance measure / Shannon's Entropy family
- k- Divergencedistance measure / Shannon's Entropy family
- Topose distance measure / Shannon's Entropy family
- Jensen Shanon distance measure / Shannon's Entropy family
- Taneja distance measure / Combinations
- Kumar Johnson distance measure / Combinations
- set based similarity measures / Set-based similarity measures

- Sigmoid function chart / The sigmoid function chart
- similarity measures
- about / Variations of gap calculations and similarity measures

- SO-PMI
- used, for finding sentiment analysis / Using SO-PMI to find sentiment analysis

- spam data
- URL / Challenge yourself!

- squared-chord family (Fidelity family)
- about / Fidelity family or squared-chord family
- Fidelity Distance measure / Fidelity family or squared-chord family
- Bhattacharya distance measure / Fidelity family or squared-chord family
- Hellinger distance measure / Fidelity family or squared-chord family
- Matusita distance measure / Fidelity family or squared-chord family
- Squared Chord distance measure / Fidelity family or squared-chord family

- Squared L2 family
- about / Squared L2 family
- Squared Euclidean distance measure / Squared L2 family
- Squared Chi distance measure / Squared L2 family
- Pearsons Chi distance measure / Squared L2 family
- Neymans Chi distance measure / Squared L2 family
- Probabilistic Symmetric Chi distance measure / Squared L2 family
- Divergence measure / Squared L2 family
- Clarks distance measure / Squared L2 family
- Additive Symmetric Chi / Squared L2 family

- Sum of Squared Error (SSE) / Linear regression method of least square
- supervised learning
- about / Different areas where machine learning is being used, Supervised machine learning
- classification problem / Supervised machine learning
- regression problem / Supervised machine learning
- training / Training and test dataset/corpus
- training dataset / Training and test dataset/corpus
- training corpus / Training and test dataset/corpus
- test dataset / Training and test dataset/corpus
- test corpus / Training and test dataset/corpus
- training data / Training and test dataset/corpus
- test data / Training and test dataset/corpus
- real life examples / Some motivating real life examples of supervised learning
- k-Nearest Neighbor (k-NN) / Nearest Neighbour algorithm (a.k.a k-NN algorithm)
- distance metrics / Distance metrics
- decision tree algorithm / Decision tree algorithms

## T

- term frequency (tf) / Information retrieval using tf-idf
- term frequency inverse document frequency (tf-idf)
- used, for retrieving information / Information retrieval using tf-idf
- about / Information retrieval using tf-idf

- top-N recommendations
- about / Top-N recommendations

- train.csv
- URL / Recognizing handwritten digits – your "Hello World" ML program

- types, anomaly detection
- point anomalies / The different types of anomalies
- contextual anomalies / The different types of anomalies
- collective anomalies / The different types of anomalies

## U

- unsupervised learning
- about / Different areas where machine learning is being used, Unsupervised learning
- features / Unsupervised learning

- User-User collaborative filtering
- about / Vocabulary of collaborative filtering
- basics / Basis of User-User collaborative filtering

- User k-Nearest Neighbors
- about / Vocabulary of collaborative filtering

## V

- vectors
- about / The basics of matrices and vectors (a short and sweet refresher)
- creating / Creating a vector

## W

- weighted linear regression
- about / Weighted linear regression

- WeightedRegression class / Weighted linear regression
- Weka
- URL / Multiclass classification using decision trees

- WekaSharp
- URL / Machine learning frameworks, Obtaining and using WekaSharp
- using / Obtaining and using WekaSharp
- obtaining / Obtaining and using WekaSharp