Index

A

• AdaBoost / Sequences of models – AdaBoost
• Additive White Gaussian Noise (AWGN)
  • about / Dimensionality reduction
• advanced nonlinear algorithms
  • about / Advanced nonlinear algorithms
  • SVM, used for classification / SVM for classification
  • SVM, used for regression / SVM for regression
  • SVM, tuning / Tuning SVM
• Anaconda
  • about / Anaconda
  • URL / Anaconda
• Arbitrary waveform generator (AWG)
  • about / Latent Factor Analysis (LFA)
• Area under a curve (AUC) / Binary classification
• arrays
  • resizing / Resizing arrays
  • deriving, from NumPy functions / Arrays derived from NumPy functions
  • obtaining, from file / Getting an array directly from a file

B

• bar graphs / Bar graphs
• Beautiful Soup
  • URL / Beautiful Soup
  • about / Beautiful Soup
• betweenness centrality / Graph algorithms
• big data
  • dealing with / Dealing with big data
  • datasets, creating as examples / Creating some big datasets as examples
  • scalability, with volume / Scalability with volume
  • velocity / Keeping up with velocity
  • variety / Dealing with variety
  • Stochastic Gradient Descent (SGD) / A quick overview of Stochastic Gradient Descent (SGD)
• big datasets
  • dealing with / Dealing with big datasets
• binary classification / Binary classification
• boxplots / Boxplots and histograms

C

• categorical data
  • working with / Working with categorical and textual data
• Chi2 object / Univariate selection
• closeness centrality / Graph algorithms
• collection of edges
  • about / Introduction to graph theory
• covariance.EllipticEnvelope class
  • about / Univariate outlier detection
• covariance matrix / The covariance matrix
• cross-validation
  • about / Cross-validation
  • working / Cross-validation
  • iterators, using / Using cross-validation iterators
  • sampling / Sampling and bootstrapping
  • bootstrapping / Sampling and bootstrapping
• curve plotting / Curve plotting

D

• data
  • loading, from CSV / Loading data directly from CSV or text files
  • loading, from text files / Loading data directly from CSV or text files
  • Scikit-learn sample generators / Scikit-learn sample generators
  • preprocessing, with pandas / Data loading and preprocessing with pandas, Data preprocessing
  • loading, with pandas / Fast and easy data loading
  • processing, with NumPy / Data processing with NumPy
  • extracting, from pandas / Extracting data from pandas
• data-text
  • about / A special type of data – text
• data formats
  • accessing / Accessing other data formats
• data learning representation
  • about / Advanced data learning representation
  • learning curve / Learning curves
  • validation curves / Validation curves
  • variables, selecting / Feature importance
  • GBT partial dependence plot / GBT partial dependence plot
• data munging phase
  • about / The data science process
• data repository
  • URL / The MLdata.org public repository
• data science
  • about / Introducing data science and Python
• data science process
  • about / The data science process
• data selection
  • about / Data selection
• datasets
  • about / Datasets and code used in the book
  • Scikit-learn toy datasets / Scikit-learn toy datasets
  • Mldata.org public repository / The MLdata.org public repository
  • LIBSVM data examples / LIBSVM data examples
• DBSCAN
  • about / An overview of unsupervised learning
• degree centrality / Graph algorithms
• dimensionality reduction
  • about / Dimensionality reduction
  • covariance matrix / The covariance matrix
  • PCA / Principal Component Analysis (PCA)
  • PCA variation, for big data-randomized PCA / A variation of PCA for big data – RandomizedPCA
  • LFA / Latent Factor Analysis (LFA)
  • LDA / Linear Discriminant Analysis (LDA)
  • LSA / Latent Semantical Analysis (LSA)
  • ICA / Independent Component Analysis (ICA)
  • kernel PCA / Kernel PCA
  • RBM / Restricted Boltzmann Machine (RBM)

E

• EDA
  • about / Introducing EDA
• eigenvector centrality / Graph algorithms
• EllipticEnvelope function / EllipticEnvelope
• ensemble strategies
  • about / Ensemble strategies
  • averaging algorithms / Ensemble strategies
  • boosting algorithms / Ensemble strategies
  • random samples, pasting by / Pasting by random samples
  • weak ensembles, bagging with / Bagging with weak ensembles
  • Random Subspaces / Random Subspaces and Random Patches
  • Random Patches / Random Subspaces and Random Patches
  • AdaBoost / Sequences of models – AdaBoost
  • GTB / Gradient tree boosting (GTB)
  • big data / Dealing with big data
• Enthought Canopy
  • URL / Enthought Canopy
  • about / Enthought Canopy
• Explorative Data Analysis (EDA)
  • about / Selected graphical examples with pandas

F

• feature creation
  • about / Feature creation
• feature selection
  • about / Feature selection
  • univariate selection / Univariate selection
  • recursive elimination / Recursive elimination
  • stability / Stability and L1-based selection
  • L1-based selection / Stability and L1-based selection
• file
  • arrays, obtaining from / Getting an array directly from a file
• functions
  • scoring / Scoring functions
  • multilabel classification / Multilabel classification
  • binary classification / Binary classification
  • regression / Regression
• f_classif object / Univariate selection
• f_regression object / Univariate selection

G

• GBT partial dependence plot / GBT partial dependence plot
• Gensim
  • about / Gensim
  • URL / Gensim
• Gephi
  • URL / Graph loading, dumping, and sampling
• graph
  • loading / Graph loading, dumping, and sampling
  • dumping / Graph loading, dumping, and sampling
  • sampling / Graph loading, dumping, and sampling
• graph algorithms
  • about / Graph algorithms
  • betweenness centrality / Graph algorithms
  • degree centrality / Graph algorithms
  • closeness centrality / Graph algorithms
  • eigenvector centrality / Graph algorithms
• graphical examples, with pandas
  • about / Selected graphical examples with pandas
  • histograms / Boxplots and histograms
  • boxplots / Boxplots and histograms
  • scatterplots / Scatterplots
  • parallel coordinates / Parallel coordinates
• Graph Modeling Language (GML)
  • about / Graph loading, dumping, and sampling
• graph theory
  • about / Introduction to graph theory
• GTB / Gradient tree boosting (GTB)

H

• hashing trick
  • about / Dealing with variety
• heterogeneous lists, NumPy arrays / Heterogeneous lists
• histograms / Histograms, Boxplots and histograms
• hyper-parameters
  • max_features / Random Subspaces and Random Patches
  • min_samples_leaf / Random Subspaces and Random Patches
  • bootstrap / Random Subspaces and Random Patches
  • n_estimators / Random Subspaces and Random Patches
• hyper-parameters optimization
  • about / Hyper-parameters' optimization
  • custom scoring functions, building / Building custom scoring functions
  • grid search runtime, reducing / Reducing the grid search runtime

I

• ICA / Independent Component Analysis (ICA)
• image visualization / Image visualization
• incremental learning
  • about / Scalability with volume
• index
  • about / Data selection
• indexing, with NumPy arrays / Slicing and indexing with NumPy arrays
• IPython
  • URL / IPython
  • about / Introducing IPython
• IPython Notebook
  • about / The IPython Notebook
• iterators
  • StratifiedKFold / Using cross-validation iterators
  • LeaveOneOut / Using cross-validation iterators
  • LeavePOut / Using cross-validation iterators
  • LeaveOneLabelOut / Using cross-validation iterators
  • LeavePLabelOut / Using cross-validation iterators

K

• k-Nearest Neighbors
  • about / The k-Nearest Neighbors
• kernel PCA / Kernel PCA

L

• Latent Dirichlet Allocation (LDA)
  • about / Gensim
• Latent Semantic Analysis (LSA)
  • about / Gensim
• LDA / Linear Discriminant Analysis (LDA)
• learning curve / Learning curves
• LFA / Latent Factor Analysis (LFA)
• LIBSVM
  • about / The MLdata.org public repository
• LIBSVM data examples
  • about / LIBSVM data examples
  • URL / LIBSVM data examples
• linear regression
  • about / Linear and logistic regression
• lists
  • transforming, to unidimensional arrays / From lists to unidimensional arrays
  • transforming, to multidimensional arrays / From lists to multidimensional arrays
• logistic regression
  • about / Linear and logistic regression
• LSA / Latent Semantical Analysis (LSA)

M

• mask
  • about / Data preprocessing
• matplotlib
  • about / Matplotlib, Introducing the basics of matplotlib
  • URL / Matplotlib
  • curve plotting / Curve plotting
  • panels, using / Using panels
  • scatterplots / Scatterplots
  • histograms / Histograms
  • bar graphs / Bar graphs
  • image visualization / Image visualization
• matrix operations, NumPy / Matrix operations
• Mean Absolute Error (MAE)
  • about / Feature creation
• Mldata.org public repository / The MLdata.org public repository
• multidimensional arrays
  • lists, transforming to / From lists to multidimensional arrays
• multilabel classification / Multilabel classification
  • about / Multilabel classification
  • Confusion matrix / Multilabel classification
  • Accuracy / Multilabel classification
  • Precision / Multilabel classification
  • Recall / Multilabel classification
  • F1 Score / Multilabel classification

N

• 20newsgroup
  • URL / A special type of data – text
• n-dimensional array
  • about / NumPy's n-dimensional array
• Naive Bayes
  • about / Naive Bayes
• Named Entity Recognition (NER) / Named Entity Recognition (NER)
• ndarray object class
  • attributes / NumPy's n-dimensional array
• ndarray objects
  • drawbacks / NumPy's n-dimensional array
  • basics / The basics of NumPy ndarray objects
• NetworkX
  • about / NetworkX
  • URL / NetworkX
• NLP
  • about / A peek into Natural Language Processing (NLP)
  • word tokenization / Word tokenization
  • stemming / Stemming
  • Word Tagging / Word Tagging
  • Named Entity Recognition (NER) / Named Entity Recognition (NER)
  • stopwords / Stopwords
  • text classification / A complete data science example – text classification
• NLTK
  • about / NLTK, A peek into Natural Language Processing (NLP)
  • URL / NLTK
• NumPy
  • about / NumPy
  • URL / NumPy
  • data, processing with / Data processing with NumPy
  • n-dimensional array / NumPy's n-dimensional array
  • URL, for user guide / Controlling the memory size
  • operations / NumPy fast operation and computations
  • computations / NumPy fast operation and computations
  • matrix operations / Matrix operations
• NumPy arrays
  • creating / Creating NumPy arrays
  • memory size, controlling / Controlling the memory size
  • heterogeneous lists / Heterogeneous lists
  • slicing with / Slicing and indexing with NumPy arrays
  • indexing with / Slicing and indexing with NumPy arrays
  • stacking / Stacking NumPy arrays
• NumPy functions
  • arrays, deriving from / Arrays derived from NumPy functions

O

• OneClassSVM
  • about / OneClassSVM
  • kernel / OneClassSVM
  • degree / OneClassSVM
  • gamma / OneClassSVM
  • nu / OneClassSVM
• outliers
  • detecting / The detection and treatment of outliers
  • treatment / The detection and treatment of outliers
  • univariate outlier detection / Univariate outlier detection
  • EllipticEnvelope function / EllipticEnvelope
  • OneClassSVM / OneClassSVM

P

• pandas
  • data, preprocessing with / Data loading and preprocessing with pandas, Data preprocessing
  • data, loading with / Fast and easy data loading
  • data, extracting from / Extracting data from pandas
• Pandas
  • about / pandas
  • URL / pandas
• panels
  • using / Using panels
• parallel coordinates / Parallel coordinates
• parameters
  • n_iter / A quick overview of Stochastic Gradient Descent (SGD)
  • penalty / A quick overview of Stochastic Gradient Descent (SGD)
  • alpha / A quick overview of Stochastic Gradient Descent (SGD)
  • l1_ratio / A quick overview of Stochastic Gradient Descent (SGD)
  • learning_rate / A quick overview of Stochastic Gradient Descent (SGD)
  • epsilon / A quick overview of Stochastic Gradient Descent (SGD)
  • shuffle / A quick overview of Stochastic Gradient Descent (SGD)
• PASCAL
  • about / The MLdata.org public repository
• PCA / Principal Component Analysis (PCA)
• pip
  • URL / The installation of packages
• problematic data
  • dealing with / Dealing with problematic data
• PyPI
  • URL / A glance at the essential Python packages
• PyPy
  • about / PyPy
• Python
  • about / Introducing data science and Python
  • characteristics / Introducing data science and Python
  • installing / Installing Python, Step-by-step installation
  • 2 / Python 2 or Python 3?
  • 3 / Python 2 or Python 3?
• Python 2 / Python 2 or Python 3?
• Python 3 / Python 2 or Python 3?
• Python packages
  • about / A glance at the essential Python packages
  • NumPy / NumPy
  • SciPy / SciPy
  • Pandas / pandas
  • Scikit-learn / Scikit-learn
  • IPython / IPython
  • matplotlib / Matplotlib
  • statsmodels / Statsmodels
  • Beautiful Soup / Beautiful Soup
  • NetworkX / NetworkX
  • NLTK / NLTK
  • Gensim / Gensim
  • PyPy / PyPy
  • installing / The installation of packages
  • URL / The installation of packages
  • upgrades / Package upgrades
• PythonXY
  • about / PythonXY
  • URL / PythonXY

R

• Radial Basis Function
  • about / SVM for classification
• random forests
  • about / The IPython Notebook
• Random Patches / Random Subspaces and Random Patches
• Random Subspaces / Random Subspaces and Random Patches
• RBF kernel
  • about / SVM for classification
• RBM / Restricted Boltzmann Machine (RBM)
• Receiver Operating Characteristics curve (ROC) / Binary classification
• recursive elimination / Recursive elimination
• regression
  • about / Regression
  • MAE / Regression
  • MSE / Regression
  • R2 score / Regression

S

• scatterplots / Scatterplots, Scatterplots
• scientific distributions
  • about / Scientific distributions
  • Anaconda / Anaconda
  • Enthought Canopy / Enthought Canopy
  • PythonXY / PythonXY
  • WinPython / WinPython
• Scikit-learn
  • about / Scikit-learn
  • URL / Scikit-learn
• Scikit-learn sample generators / Scikit-learn sample generators
• Scikit-learn toy datasets
  • about / Scikit-learn toy datasets
  • methods / Scikit-learn toy datasets
• Scikit-learn website
  • URL / Using cross-validation iterators
• SciPy
  • about / SciPy
  • URL / SciPy
• SGDClassifier
  • about / A quick overview of Stochastic Gradient Descent (SGD)
• SGDRegressor
  • about / A quick overview of Stochastic Gradient Descent (SGD)
• Silhouette Coefficient
  • URL / An overview of unsupervised learning
• Singular Value Decomposition (SVD)
  • about / A variation of PCA for big data – RandomizedPCA
• slicing, with NumPy arrays / Slicing and indexing with NumPy arrays
• statsmodels
  • about / Statsmodels
  • URL / Statsmodels
• stemming / Stemming
• Stochastic Gradient Descent (SGD) / A quick overview of Stochastic Gradient Descent (SGD)
• stopwords / Stopwords
• Support Vector Machine (SVM)
  • about / The IPython Notebook
• SVM
  • about / Advanced nonlinear algorithms
  • tuning / Tuning SVM
  • parameters / Tuning SVM
• svm.OneClassSVM class
  • about / Univariate outlier detection

T

• testing
  • about / Testing and validating
• text
  • about / A special type of data – text
• text classification / A complete data science example – text classification
• textual data
  • working with / Working with categorical and textual data

U

• UCI repository
  • URL / SVM for classification
• unidimensional arrays
  • lists, transforming to / From lists to unidimensional arrays
• univariate selection / Univariate selection
• unsupervised learning
  • about / An overview of unsupervised learning

V

• validating
  • about / Testing and validating
• validation curves / Validation curves
• variables
  • selecting / Feature importance
• variety
  • about / Dealing with big data
• velocity
  • about / Dealing with big data
• veracity
  • about / Dealing with big data
• visualization rules
  • URL / Introducing the basics of matplotlib
• volume
  • about / Dealing with big data

W

• WinPython
  • URL / WinPython
  • about / WinPython
• WinPython Package Manager (WPPM)
  • about / WinPython
• Word Tagging / Word Tagging
• word tokenization / Word tokenization