Index

A

• abstraction / Structured features
• adaptive boosting (AdaBoost) / Adaboost
• agglomerative / Discretization
• Anaconda
  • reference link / Installing the SciPy stack
• anomaly detection / Dimensionality reduction
• approaches, data volume issue
  • efficiency / Data volume
  • scalability / Data volume
  • parallelism / Data volume
• approaches, machine learning problem
  • exploratory / Types of questions
  • descriptive / Types of questions
  • inferential / Types of questions
  • predictive / Types of questions
  • casual / Types of questions
  • mechanistic / Types of questions
• approaches, Sklearn
  • estimator score / Evaluating model performance
  • scoring parameters / Evaluating model performance
  • metric functions / Evaluating model performance
• association rule learning / Set-based rule models
• association rule mining / Set-based rule models
• audioSamp.wav file
  • download link / Data from sound
• averaging method / Ensemble types

B

• bagging
  • about / Bagging
  • random forests / Random forests
  • extra trees / Extra trees
• batch gradient descent / Gradient descent
• Bayes classifier / Operations and statistics
• beam search / Set-based rule models
• bias feature / Gradient descent
• Big Data
  • about / Big data
  • challenges / Challenges of big data
  • data models / Data models
  • data distributions / Data distributions
  • data, from distributions / Data distributions
  • data, from databases / Data from databases
  • data, obtaining from Web / Data from the Web
  • data, obtaining from natural language / Data from natural language
  • data, obtaining from images / Data from images
  • data, obtaining from application programming interfaces / Data from application programming interfaces
• bin / Discretization
• binarization / Transforming features
• binary splits / Features
• Boolean feature / Categorical features
• boosting
  • about / Boosting
  • Adaboost (adaptive boosting ) / Adaboost
  • gradient boosting / Gradient boosting
• boosting method / Ensemble types
• broadcasting / Mathematical operations
• broad settings, tasks
  • supervised learning / Tasks
  • unsupervised learning / Tasks
  • reinforcement learning / Tasks
• bucketing / Other methods

C

• calibration / Calibration
• Canopy
  • reference link / Installing the SciPy stack
• categorical features / Categorical features
• central moment / Operations and statistics
• challenges, Big Data
  • about / Challenges of big data
  • data volume / Data volume
  • data velocity / Data velocity
  • data variety / Data variety
• classification error / Purity
• closed form solution / Regularization
• collaborative filtering approach / Building a recommender system, Collaborative filtering
• computational complexity / PAC learning and computational complexity
• conjunctively separable / Version space
• content based filtering approach / Content-based filtering
• corpora / Data from natural language
• cost function
  • about / Cost function
  • minimizing / Minimizing the cost function
• cost function, logistic regression / The Cost function for logistic regression
• coverage space / Coverage space
• Cumulative Density Function (CDF) / Data distributions

D

• data
  • reference link / Pandas
  • about / What is data?
  • models / Data models
  • distributions / Data distributions
  • obtaining, from databases / Data from databases
  • obtaining, from Web / Data from the Web
  • obtaining, from natural language / Data from natural language
  • obtaining, from images / Data from images
  • obtaining, from application programming interfaces / Data from application programming interfaces
  • cleaning / Cleaning data
  • visualizing / Visualizing data
• databases
  • used, for solving issues / Data models
• data models
  • about / Data models
• data models, components
  • structure / Data models
  • constraints / Data models
  • operations / Data models
• decision boundary / Logistic regression
• deep architecture / Other neural net architectures
• descriptive models / Set-based rule models
• design principles
  • about / Design principles
  • question, types / Types of questions
  • right question, asking / Are you asking the right question?
  • tasks / Tasks
  • unified modelling language (UML) / Unified modeling language
• discretization / Discretization
• distro / Installing the SciPy stack
• divisive / Discretization
• downhill simplex algorithm / SciPy

E

• ElasticNet / Gradient descent
• ensemble
  • types / Ensemble types
  • strategies / Ensemble strategies
  • techniques / Other methods
• ensemble techniques
  • about / Ensemble types
  • averaging method / Ensemble types
  • boosting method / Ensemble types
• entering variable / Linear programming
• equal frequency discretization / Discretization
• equal width discretization / Discretization
• extra trees
  • about / Extra trees
• ExtraTreesClassifier class / Extra trees
• ExtraTreesRegressor class / Extra trees

F

• feature
  • types / Feature types
  • transforming / Transforming features
  • discretization / Discretization
  • normalization / Normalization
  • calibration / Calibration
• feature types
  • quantitative features / Quantitative features
  • ordinal features / Ordinal features
  • categorical features / Categorical features
• Fourier Transform / Signals
• functions, for impurity measures
  • Gini index / Purity
  • Entropy / Purity

G

• global / Gradient descent
• gradient boosting / Gradient boosting
• gradient checking / Gradient checking

H

• Hadoop / Data volume
• hamming distance / Ordinal features
• human interface
  • about / The human interface
• hyper parameter / Gradient descent
• hypothesis space / Logical models

I

• imputation / Calibration
• inductive logic programming
  • about / Structured features
• instance space / Logical models
• integrated pest management systems greenhouses
  • about / Insect detection in greenhouses
  • reviewing / Reviewing the case study
• internal disjunction / Version space
• IPython console
  • about / IPython console
• item-based collaborative filtering / Collaborative filtering

J

• Jupyter
  • reference link / IPython console

K

• k-fold cross validation / Evaluating model performance
• kernel trick / Features
• k nearest neighbours (K-NN)
  • about / Scikit-learn
  • Sklearn. KNeighboursClassifier / Scikit-learn
  • RadiusNeighboursClassifier / Scikit-learn
• kurtosis / Operations and statistics

L

• L1 norm / Regularization
• lambda / Regularization
• lambda(λ) / Data distributions
• Large Hadron Collider / Data velocity
• Lasso / Gradient descent
• lasso regression / Regularization
• learning curves
  • using / Learning curves
• least general generalization (LGG) / Generality ordering
• least squares
  • about / Introducing least squares
  • gradient descent / Gradient descent
  • normal equation / The normal equation
• leaving variable / Linear programming
• Linear programming (LP) / Linear programming
• local minimum / Gradient descent
• logical models
  • about / Logical models
  • generality ordering / Generality ordering
  • version space / Version space
  • coverage space / Coverage space
  • Probably Approximately Correct (PAC) learning / PAC learning and computational complexity
  • computational complexity / PAC learning and computational complexity
• logistic calibration / Calibration
• logistic function / Logistic regression
• logistic regression
  • about / Logistic regression
  • cost function / The Cost function for logistic regression
• logistic units
  • about / Logistic units

M

• machine learning
  • about / Machine learning at a glance
• machine learning systems
  • activities / Design principles
• MapReduce / Data volume
• margin / Ensemble strategies
• mathematical operations, NumPy
  • about / Mathematical operations
  • vectors / Mathematical operations
  • polynomial functions / Mathematical operations
• Matlab / Python for machine learning
• Matplotlib
  • about / Installing the SciPy stack, Matplotlib
• mean (µ (mu)) / Data distributions
• meta-estimators / Multiclass classification
• meta-learning / Other methods
• meta-model / Other methods
• MNIST dataset
  • reference link / Implementing a neural network
• model performance
  • evaluating / Evaluating model performance
  • measuring, with precision (P) / Evaluating model performance
  • measuring, with recall (R) / Evaluating model performance
• models
  • about / Models
  • grouping approach / Models
  • geometric models / Geometric models
  • probabilistic models / Probabilistic models
  • logical models / Logical models
  • rule models / Logical models, Rule models
  • tree models / Tree models
• model selection
  • about / Model selection
  • Gridsearch, using / Gridsearch
• Moore's law / Data volume
• Mr Clippy / The human interface
• multiclass classification
  • about / Multiclass classification
  • one versus all technique / Multiclass classification
  • one versus one technique / Multiclass classification
• multicollinearity / Scikit-learn
• MySQL server
  • reference link / Data from databases

N

• Natural Language Tool Kit (NLTK) / Data from natural language
• nelder-mead solver / SciPy
• neural net architectures
  • about / Other neural net architectures
  • deep architecture / Other neural net architectures
  • recurrent neural networks (RNNs) / Other neural net architectures
• neural network
  • starting with / Getting started with neural networks
  • implementing / Implementing a neural network
• Ngram
  • reference link / The human interface
• normal equation / The normal equation
• normalization / Normalization
• NumPy
  • about / Installing the SciPy stack, NumPY
  • arrays, constructing / Constructing and transforming arrays
  • arrays, transforming / Constructing and transforming arrays
  • mathematical operations / Mathematical operations

O

• one hot encoding / Transforming features
• one versus all technique / Multiclass classification
• one versus one technique / Multiclass classification
• operations / Operations and statistics
• ordered list approach / The ordered list approach
• ordinal features / Ordinal features
• Ordinary Least Squares / Scikit-learn
• orthogonal function / Signals

P

• packages, SciPy
  • cluster / SciPy
  • constants / SciPy
  • integrate / SciPy
  • interpolate / SciPy
  • io / SciPy
  • optimize / SciPy
  • linalg / SciPy
  • ndimage / SciPy
  • odr / SciPy
  • stats / SciPy
• Pandas
  • about / Pandas
• parameters, bagging
  • base_estimator / Bagging
  • n_estimators / Bagging
  • max_samples / Bagging
  • max_features / Bagging
  • bootstrap / Bagging
  • bootstrap_features / Bagging
• pdfminer3k / Cleaning data
• pdftotext / Cleaning data
• pivoting / Linear programming
• polynomial regression / Gradient descent
• Portable Document Format (PDF) / Cleaning data
• principle component analysis (PCA)
  • about / Principle component analysis
• Principle Component Analysis (PCA) / Features, Scikit-learn
• probability density function / Data distributions
• Probably Approximately Correct (PAC) learning / PAC learning and computational complexity
• Python
  • used, for machine learning / Python for machine learning

Q

• quantitative features / Quantitative features

R

• R / Python for machine learning
• random forest
  • about / Random forests
• real-world case studies
  • about / Real-world case studies
  • recommender system, building / Building a recommender system
  • integrated pest management systems greenhouses / Insect detection in greenhouses
• Receiver Operator Characteristic (ROC) / Calibration
• recommender system
  • building / Building a recommender system
  • content-based filtering approach / Content-based filtering
  • collaborative filtering approach / Collaborative filtering
  • reviewing / Reviewing the case study
• recurrent neural networks (RNNs) / Other neural net architectures
• regularization
  • about / Regularization
• Ridge / Gradient descent
• ridge regression / Regularization
• Rosenbrock function / SciPy
• rule models
  • purity / Rule models
  • ordered list approach / The ordered list approach
  • set-based rule models / Set-based rule models

S

• sample complexity / PAC learning and computational complexity
• scikit-learn
  • about / Scikit-learn
• SciPy
  • about / SciPy
  • packages / SciPy
  • reference link / SciPy
• SciPy stack
  • installing / Installing the SciPy stack
  • reference link / Installing the SciPy stack
  • about / Installing the SciPy stack
• segment / Models
• set-based rule models / Set-based rule models
• SGDClassifier / Gradient descent
• SGDRegressor / Gradient descent
• shrinkage / Scikit-learn
• sigmoid function / Logistic regression
• signals
  • about / Signals
  • data, obtaining from sound / Data from sound
• similarity function / Features
• skewness / Operations and statistics
• stacked generalization / Other methods
• stacking / Other methods
• standard deviation (δ (sigma)) / Data distributions
• standardized features / Calibration
• statistics
  • about / Operations and statistics
  • dispersion / Operations and statistics
  • shape / Operations and statistics
  • central tendency / Operations and statistics
• Stochastic gradient descent / Gradient descent
• stratified cross validation / Evaluating model performance
• streaming processing / Data velocity
• structured features
  • about / Structured features
• subgroup discovery / Set-based rule models
• subspace sampling / Bagging, Random forests
• sum of the squared error / Introducing least squares
• Support Vector Machines (SVM) / Features

T

• task classification
  • binary classification / Classification
  • multiclass classification / Classification
• tasks
  • about / Tasks
  • classification / Classification
  • regression / Regression
  • clustering / Clustering
  • dimensionality reduction / Dimensionality reduction
  • errors / Errors
  • optimization problems / Optimization
  • linear programming / Linear programming
  • models / Models
  • features / Features
• term-document matrix / Content-based filtering
• Thrip / Visualizing data
• tree models
  • about / Tree models
  • purity / Purity

U

• unified modelling language (UML)
  • about / Unified modeling language
  • class diagrams / Class diagrams
  • object diagrams / Object diagrams
  • activity diagrams / Activity diagrams
  • state diagrams / State diagrams

V

• version space / Version space

W

• weak learnability / Boosting
• Whitefly / Visualizing data
• WordNet
  • reference link / The human interface
• world database / Data from databases