Index
A
- A/B tests
- Abstract-C / Abstract-C
- activation function
- active learning
- active learning, case study
- about / Case study in active learning
- tools / Tools and software
- software / Tools and software
- business problem / Business problem
- machine learning, mapping / Machine learning mapping
- data collection / Data Collection
- data sampling / Data sampling and transformation
- data transformation / Data sampling and transformation
- feature analysis / Feature analysis and dimensionality reduction
- dimensionality reduction / Feature analysis and dimensionality reduction
- models / Models, results, and evaluation
- results / Models, results, and evaluation
- evaluation / Models, results, and evaluation
- results, pool-based scenarios / Pool-based scenarios
- results, stream-based scenarios / Stream-based scenarios
- results, analysis / Analysis of active learning results
- activity recognition
- AdaBoost M1 method
- ADaptable sliding WINdow (ADWIN)
- adaptation methods
- Advanced Message Queueing Protocol (AMQP) / Message queueing frameworks
- advanced modelling
- affinity analysis
- affinity propagation
- agglomerative clustering
- algorithms, comparing
- Amazon Elastic MapReduce (EMR) / Amazon Elastic MapReduce
- Amazon Kinesis / Publish-subscribe frameworks
- Amazon Machine Learning / Machine learning as a service
- Amazon Redshift / Amazon Redshift
- analysis types
- Android Device Monitor
- Android Studio
- Angle-based Outlier Degree (ABOD) / How does it work?
- anomalous behaviour detection
- anomalous pattern detection
- anomaly detection
- anomaly detection, in time series data
- anomaly detection, in website traffic
- ANOVA test / ANOVA test
- Apache Kafka / Publish-subscribe frameworks
- Apache Mahout
- Apache Spark
- Apache Storm / SAMOA as a real-time Big Data Machine Learning framework
- Application Portfolio Management (APM)
- Applied Machine Learning
- Approx Storm / Approx Storm
- Apriori
- Apriori algorithm
- ArangoDB / Graph databases
- artificial neural networks
- association analysis / Machine learning – types and subtypes
- association rule learning
- association rule learning, basic concepts
- autoencoder
- Autoencoders
- axioms of probability / Axioms of probability
B
C
D
- (DBSCAN)
- D-Separation, Bayesian networks / D-Separation
- data
- data acquisition
- data analysis
- data and problem definition
- Data and problem definition
- data cleaning
- data collection
- data collector
- data distribution sampling
- Data Frame / H2O architecture
- data management
- Data Mining
- Data Mining Research
- data pre-processing
- data preparation
- data preprocessing
- data processing
- data quality analysis / Data quality analysis
- data reduction
- data sampling
- about / Data sampling, Data sampling and transformation
- need for / Is sampling needed?
- undersampling / Undersampling and oversampling
- oversampling / Undersampling and oversampling
- stratified sampling / Stratified sampling
- techniques / Training, validation, and test set
- experiments / Experiments, results, and analysis
- results / Experiments, results, and analysis
- analysis / Experiments, results, and analysis, Feature relevance and analysis
- feature relevance / Feature relevance and analysis
- test data, evaluation / Evaluation on test data
- results, analysis / Analysis of results
- data science
- Data Science Central
- Data Science CS109 (Harvard) by John A. Paulson
- data scientist
- dataset rebalancing
- datasets
- datasets, machine learning
- data storage
- data transformation
- about / Data transformation, Data transformation and preprocessing, Data sampling and transformation
- feature, construction / Feature construction
- missing values, handling / Handling missing values
- outliers, handling / Outliers
- discretization / Discretization
- data sampling / Data sampling
- training / Training, validation, and test set
- validation / Training, validation, and test set
- test set / Training, validation, and test set
- Davies-Bouldin index / Davies-Bouldin index
- Decision and Predictive Analytics (ADAPA) / Predictive Model Markup Language
- decision trees
- Decision Trees
- decision trees learning
- Deep Autoencoders
- deep belief network
- deep belief networks
- Deep Belief Networks (DBN)
- Deep Belief Networks (DBNs)
- deep convolutional networks
- Deep feed-forward NN
- deep learning
- Deep Learning
- Deep Learning (DL) / Feature relevance and analysis
- deep learning, case study
- Deeplearning4j
- DeepLearning4J
- deeplearning4java
- delta rule
- denisity
- density-based methods / Outliers
- density based algorithm
- desccriptive quality analysis
- detection methods
- Deviance-Threshold Measure / Measures to evaluate structures
- Dice coefficient / Dice coefficient
- dimensionality reduction / Dimensionality reduction
- Directed Acyclic Graph (DAG) / Definition
- directory
- Direct Update of Events (DUE) / Direct Update of Events (DUE)
- Dirichlet distribution / Prior and posterior using the Dirichlet distribution
- Discrete Fourier Transform (DFT)
- discretization
- distance-based clustering
- distance-based methods / Outliers
- distance measures
- distribution changes, monitoring
- divide-and-conquer strategy
- document collection
- document databases / Document databases
- document frequency (DF) / Frequency-based techniques
- double evaluateLeftToRight method
- drift detection
- drift detection method (DDM) / Drift Detection Method or DDM
- DrivenData / Competitions
- DropConnect neural network
- Dropouts
- DSGuide
- Dunns Indices / Dunn's Indices
- dynamic time wrapping (DTW)
E
F
- F-Measure / F-Measure
- False Positive Rate (FPR) / Confusion matrix and related metrics
- feature analysis
- feature evaluation techniques
- Feature extraction
- feature extraction/generation
- feature map
- feature relevance analysis
- features
- feature search techniques / Feature search techniques
- feature selection
- feedforward neural networks
- file
- filter approach
- Fine Needle Aspirate (FNA) / Datasets and analysis
- flow of influence, Bayesian networks / Flow of influence
- Fourier transform
- FP-Growth
- FP-growth algorithm
- FP-tree structure
- fraud detection, of insurance claims
- frequent pattern (FP)
- Friedmans test / Friedman's test
G
H
I
J
K
L
M
- machine learning / Machine Learning
- machine learning application
- machine learning applications / Machine learning applications
- Machine Learning for Language Toolkit (MALLET)
- machine learning libraries
- Machine learning mastery
- machine translation (MT) / Machine translation
- Mahalanobis distance
- Mahout interfaces, abstractions
- Mahout libraries
- Mallet / Mallet
- mallet
- MALLET, packages
- Manhattan distance
- manifold learning
- marginal distribution / Random variables, joint, and marginal distributions
- market basket analysis (MBA)
- market data / Datasets used in machine learning
- Markov blanket / Markov blanket
- Markov chain
- Markov chains
- Markov networks (MN) / Markov networks and conditional random fields
- Markov random field (MRF) / Markov networks and conditional random fields
- massively parallel processing (MPP) / Amazon Redshift
- Massive Online Analysis (MOA)
- mathematical transformation
- matrix
- matrix product, properties
- Maven plugin
- maximum entropy Markov model (MEMM)
- Maximum Likelihood Estimates (MLE) / How does it work?
- Maximum likelihood estimation (MLE) / Maximum likelihood estimation for Bayesian networks
- McNemars Test / McNemar's Test
- McNemar test / Comparing algorithms and metrics
- mean / Mean
- mean absolute error
- mean shift
- mean squared error
- measurement scales
- meta learners
- Micro Clustering based Algorithm (MCOD) / Micro Clustering based Algorithm (MCOD)
- Microsoft Azure HDInsight / Microsoft Azure HDInsight
- Microsoft Azure Machine Learning / Machine learning as a service
- Min-Max Normalization / Outliers
- minimal redundancy maximal relevance (mRMR) / Minimal redundancy maximal relevance (mRMR)
- Minimum Covariant Determinant (MCD) / How does it work?
- Minimum Description Length (MDL) / How does it work?
- Minkowski distance
- missing values
- Mixed National Institute of Standards and Technology (MNIST) / Data quality analysis
- Mldata.org
- MLlib API library
- MNIST database
- MNIST dataset
- MOA
- mobile app
- mobile phone
- mobile phone sensors
- model
- model assesment
- model comparison
- model evaluation
- model evaluation metrics
- model evolution, monitoring
- model evolution, monitoring
- modeling techniques
- models
- model validation techniques
- MongoDB
- most probable explanation (MPE) / MAP queries and marginal MAP queries
- motion sensors
- Mozilla Thunderbird
- Multi-layered neural network
- inputs / Inputs, neurons, activation function, and mathematical notation
- neuron / Inputs, neurons, activation function, and mathematical notation
- activation function / Inputs, neurons, activation function, and mathematical notation
- mathematical notation / Inputs, neurons, activation function, and mathematical notation
- about / Multi-layered neural network
- structure and mathematical notations / Structure and mathematical notations
- activation functions / Activation functions in NN
- training / Training neural network
- multi-layered perceptron (MLP) / Feature relevance and analysis
- Multi-layer feed-forward neural network
- multi-view SSL
- Multidimensional Scaling (MDS)
- Multilayer Convolutional Network
- multinomial distribution / Random variables, joint, and marginal distributions
- multiple algorithms, comparing
- multivariate feature analysis
- multivariate feature selection
- myrunscollector package
N
O
- one-class SVM
- online bagging algorithm
- online boosting algorithm
- online courses
- Online k-Means
- online learning engine
- online linear models, with loss functions
- Online Naive Bayes
- OpenMarkov / OpenMarkov
- opinion mining / Sentiment analysis and opinion mining
- Oracle Database Online Documentation
- ordinal data
- OrientDB / Graph databases
- outlier algorithms
- outlier detection
- outlier evaluation techniques
- outlier models
- outliers
- Output layer
- overfits
- overfitting
- oversampling / Undersampling and oversampling
P
Q
- Query by Committee (QBC)
- Query by disagreement (QBD)
R
- R-Squared / R-Squared
- Radial Basis Function (RBF) / Inputs and outputs
- Rand index / Rand index
- Random Forest / Random Forest
- Random Forest (RF) / Feature relevance and analysis, Random Forest
- random projections (RP)
- RapidMiner
- ratio data
- real-time Big Data Machine Learning
- real-time stream processing / Real-time stream processing
- real-world case study
- about / Real-world case study
- tools / Tools and software
- software / Tools and software
- business problem / Business problem
- machine learning, mapping / Machine learning mapping
- data collection / Data collection
- data quality analysis / Data quality analysis
- data sampling / Data sampling and transformation
- data transformation / Data sampling and transformation
- feature analysis / Feature analysis and dimensionality reduction
- dimensionality reduction / Feature analysis and dimensionality reduction
- models, clustering / Clustering models, results, and evaluation
- results / Clustering models, results, and evaluation, Outlier models, results, and evaluation
- evaluation / Clustering models, results, and evaluation, Outlier models, results, and evaluation
- outlier models / Outlier models, results, and evaluation
- reasoning, Bayesian networks
- recall
- receiver operating characteristics (ROC) / Machine learning – concepts and terminology
- Receiver Operating Characteristics (ROC)
- recommendation engine
- Recurrent neural networks (RNN)
- regression
- regression model
- regression trees
- regularization
- reinforcement learning / Machine learning – types and subtypes
- representation, Bayesian networks
- resampling / Is sampling needed?
- Resilient Distributed Dataset (RDD)
- Resilient Distributed Datasets (RDD) / Spark architecture
- Restricted Boltzman machine
- restricted Boltzmann machine
- restricted Boltzmann machines (RBM)
- Restricted Boltzmann Machines (RBM)
- ROC curve / ROC and PRC curves
- Roc curves
- roles, machine learning
- RuleSetModel / Predictive Model Markup Language
S
- SAMOA
- sampling
- sampling-based techniques, Bayesian networks
- Samza / SAMOA as a real-time Big Data Machine Learning framework
- scalar product
- Scale Invariant Feature Transform (SIFT)
- ScatterPlot Matrix / Multivariate feature analysis
- scatter plots / Multivariate feature analysis
- score function
- self-organizing maps (SOM)
- self-training SSL
- semantic features / Semantic features
- semantic reasoning / Semantic reasoning and inferencing
- semi-supervised learning / Machine learning – types and subtypes
- Semi-Supervised Learning (SSL)
- Semi-Supervised Learning (SSL), case study
- sentiment analysis / Sentiment analysis and opinion mining
- sequential data / Datasets used in machine learning
- shrinking methods
- Sigmoid function / Sigmoid function
- Sigmoid Kernel / How does it work?
- Silhouettes index / Silhouette's index
- similar items
- similarity calculation
- similarity measures
- SimRank
- single layer regression model
- Singular value decomposition (SVD)
- Singular Value Decomposition (SVD) / Advantages and limitations
- singular value decomposition (SVD) / Dimensionality reduction, Singular value decomposition (SVD)
- sliding windows
- SMILE
- Smile
- software / Tools and software
- source-sink frameworks / Source-sink frameworks
- Spark-MLlib
- Spark core, components
- Spark MLlib
- used, as Big Data Machine Learning / Spark MLlib as Big Data Machine Learning platform
- architecture / Spark architecture
- machine learning / Machine Learning in MLlib
- tools / Tools and usage
- usage / Tools and usage
- experiments / Experiments, results, and analysis
- results / Experiments, results, and analysis
- analysis / Experiments, results, and analysis
- reference link / Experiments, results, and analysis
- k-Means / k-Means
- k-Means, with PCA / k-Means with PCA
- k-Means with PCA, bisecting / Bisecting k-Means (with PCA)
- Gaussian Mixture Model (GMM) / Gaussian Mixture Model
- Random Forest / Random Forest
- results, analysis / Analysis of results
- Spark SQL / Spark SQL
- Spark Streaming
- sparse coding
- spatio-temporal patterns
- Spearman's footrule distance
- spectral clustering
- SQL frameworks / SQL frameworks
- stacked autoencoders
- standard deviation / Standard deviation
- standardization
- standards and markup languages
- Statistical-based
- Statistics 110 (Harvard) by Joe Biltzstein
- stemming / Stemming or lemmatization
- step execution mode / Amazon Elastic MapReduce
- Stochastic Gradient Descent (SGD) / Supervised learning experiments
- stop words removal
- stratification
- stratified sampling / Stratified sampling
- stream / SAMOA architecture
- stream computational technique
- stream learning / Machine learning – types and subtypes
- stream learning, case study
- Stream Processing Engines (SPE) / Real-time stream processing
- stream processing technique
- structured data
- Structure Score Measure / Measures to evaluate structures
- subfields
- Subspace Outlier Detection (SOD) / How does it work?
- Sum of Squared Errors (SSE) / Clustering models, results, and evaluation, Experiments, results, and analysis
- sum transfer function
- supermarket dataset
- supervised learning / Machine learning – types and subtypes
- Support Vector Machine (SVM) model / Predictive Model Markup Language
- Support Vector Machines (SVM) / How does it work?
- support vector machines (SVM)
- survivorship bias
- suspicious behaviour detection
- suspicious pattern detection
- suspicious patterns, modelling
- SVM
- Syntactic features
- Syntactic Language Models (SLM)
- Synthetic Minority Oversampling Technique (SMOTE) / Undersampling and oversampling
- Sample, Explore, Modify, Model, and Assess (SEMMA).
T
U
V
W
X
Y
Z
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