Index
A
- Accord.NET
- 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
- confusion matrix
- 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
- 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
- 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
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
L
- 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
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
- SentiWordNet
- download link / A baseline algorithm for SA using SentiWordNet lexicons
- 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
- 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
- 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
- User-User collaborative filtering
- User k-Nearest Neighbors
V
- vectors
W
- weighted linear regression
- about / Weighted linear regression
- WeightedRegression class / Weighted linear regression
- Weka
- WekaSharp