Index
A
- accuracy
- evaluating, with cross validation / Getting ready…
- AdaBoost / Estimating housing prices
- Adaptive Boosting technique
- reference / Building a face detector using Haar cascades
- Affinity Propagation / Finding patterns in stock market data
- agglomerative clustering
- about / Grouping data using agglomerative clustering
- reference / Grouping data using agglomerative clustering
- used, for grouping data / How to do it…
- audio data
- reading / Reading and plotting audio data, How to do it…
- plotting / Reading and plotting audio data, How to do it…
- audio signal
- transforming, into frequency domain / Transforming audio signals into the frequency domain, How to do it…
- audio signals
- generating, custom parameters used / Generating audio signals with custom parameters, How to do it…
B
- bag-of-words model
- building / Building a bag-of-words model, How to do it…, How it works…
- bicycle demand distribution
- estimating / Estimating bicycle demand distribution, How to do it…, There's more…
- blind source separation
- about / Performing blind source separation
- performing / Performing blind source separation, How to do it…
- reference / Performing blind source separation
- bubble plots
- plotting / Plotting bubble plots, How to do it…
- animating / Animating bubble plots, How to do it…
C
- canny edge detector
- about / How to do it…
- URL / How to do it…
- cars
- evaluating, based on characteristics / Evaluating cars based on their characteristics, Getting ready, How to do it…
- characters
- visualizing, in optical character recognition database / Visualizing the characters in an optical character recognition database, How to do it…
- chunking
- used, for dividing text / Dividing text using chunking, How to do it…
- about / Dividing text using chunking
- classifier
- constructing / Introduction
- class imbalance
- tackling / Tackling class imbalance, How to do it…
- clustering
- about / Introduction
- clustering algorithms
- performance, evaluating / Evaluating the performance of clustering algorithms, How to do it…
- color scheme options
- reference / How to do it…
- Computer Vision
- about / Introduction
- conditional random fields (CRFs)
- about / Building Conditional Random Fields for sequential text data
- building, for sequential text data / Building Conditional Random Fields for sequential text data, Getting ready, How to do it…
- confidence measurements
- extracting / Extracting confidence measurements, How to do it…
- confusion matrix
- visualizing / Visualizing the confusion matrix, How to do it…
- about / Visualizing the confusion matrix
- corner detection
- about / Detecting corners, How to do it…
- cross validation
- about / Evaluating the accuracy using cross-validation
- used, for evaluating accuracy / Evaluating the accuracy using cross-validation, Getting ready…
- customer segmentation model
- building / Building a customer segmentation model, How to do it…
- custom parameters
- used, for generating audio signals / Generating audio signals with custom parameters, How to do it…
D
- 3D scatter plots
- plotting / Plotting 3D scatter plots
- data
- preprocessing, different techniques used / Preprocessing data using different techniques, How to do it…
- preprocessing ways / How to do it…
- clustering, k-means algorithm used / Clustering data using the k-means algorithm, How to do it…
- gourping, agglomerative clustering used / Grouping data using agglomerative clustering, How to do it…
- preprocessing, tokenization used / Preprocessing data using tokenization, How to do it…
- transforming, into time series format / Transforming data into the time series format, How to do it…
- data preprocessing ways
- mean removal / Mean removal
- scaling / Scaling
- normalization / Normalization
- binarization / Binarization
- One Hot Encoding / One Hot Encoding
- dataset
- splitting, for training and testing / Splitting the dataset for training and testing, How to do it…
- reference / Building an event predictor, Getting ready, Visualizing the characters in an optical character recognition database
- similar users, finding / Finding similar users in the dataset, How to do it…
- dataset attributes
- buying / Getting ready
- maint / Getting ready
- doors / Getting ready
- persons / Getting ready
- lug_boot / Getting ready
- safety / Getting ready
- data visualization
- about / Introduction
- date-formatted time series data
- DBSCAN algorithm
- about / Automatically estimating the number of clusters using DBSCAN algorithm
- used, for estimating number of clusters / Automatically estimating the number of clusters using DBSCAN algorithm, How to do it…
- reference / Automatically estimating the number of clusters using DBSCAN algorithm
- decision tree regressor / Estimating housing prices
- deep neural network
- building / Building a deep neural network, How to do it…
- dynamic signals
- animating / Animating dynamic signals, How to do it…
E
- edge detection
- about / Detecting edges
- performing / Detecting edges, How to do it…
- empty squares / Getting ready
- epsilon
- Euclidean distance score
- computing / Computing the Euclidean distance score, How to do it…
- event predictor
- building / Building an event predictor, Getting ready, How to do it…
- exemplars
- reference / Finding patterns in stock market data
- extremely random forests (ERFs)
- about / Training an image classifier using Extremely Random Forests
- used, for training image classifier / Training an image classifier using Extremely Random Forests, How to do it…
- reference / Training an image classifier using Extremely Random Forests
- eye and nose detectors
- building / Building eye and nose detectors, How to do it…
F
- face dataset
- face detector
- building, Haar cascades used / Building a face detector using Haar cascades, How to do it…
- face recognition
- about / Introduction
- face recognizer
- building, Local Binary Patterns Histograms used / Building a face recognizer using Local Binary Patterns Histogram, How to do it…
- features
- creating, visual codebook and vector quantization used / Creating features using visual codebook and vector quantization, How to do it…
- Fourier transforms
- frequency domain
- audio signal, transforming / Transforming audio signals into the frequency domain, How to do it…
- frequency domain features
- extracting / Extracting frequency domain features, How to do it…
- function compositions
- building, for data processing / Building function compositions for data processing, How to do it…
G
- gender
- identifying / Identifying the gender, How to do it…
- gradient descent
- reference / How to do it…
H
- Haar cascades
- about / Building a face detector using Haar cascades
- used, for face detection / Building a face detector using Haar cascades, How to do it…
- Harris corner detector function
- reference / How to do it…
- heat maps
- visualizing / Visualizing heat maps, How to do it…
- hidden layers
- about / Introduction
- hidden Markov models (HMMs)
- building / Building Hidden Markov Models, How to do it…
- URL / Building Hidden Markov Models
- about / Building Hidden Markov Models for sequential data
- building, for sequential data / Building Hidden Markov Models for sequential data, How to do it…
- used, for analyzing stock market data / Analyzing stock market data using Hidden Markov Models, How to do it…
- hierarchical clustering
- histogram equalization
- about / Histogram equalization, How to do it…
- histograms
- plotting / Plotting histograms, How to do it…
- housing prices
- estimating / Estimating housing prices, Getting ready, How to do it…
- hyperparameters
- about / Extracting validation curves
- hypotrochoid / How to do it…
I
- image
- compressing, vector quantization used / Compressing an image using vector quantization, How to do it…
- image classifier
- training, extremely random forests used / Training an image classifier using Extremely Random Forests, How to do it…
- images
- operating, OpenCV-Python used / Operating on images using OpenCV-Python, How to do it…
- income bracket
- estimating / Estimating the income bracket, How to do it…
- inverse document frequency (IDF) / Building a text classifier, How it works…
K
- k-means algorithm
- about / Clustering data using the k-means algorithm
- used, for clustering data / Clustering data using the k-means algorithm, How to do it…
- reference / Clustering data using the k-means algorithm
- k-means clustering
- about / How to do it…
- k-nearest neighbors
- k-nearest neighbors classifier
- constructing / Constructing a k-nearest neighbors classifier, How to do it…, How it works…
- k-nearest neighbors regressor
- constructing / Constructing a k-nearest neighbors regressor, How to do it…, How it works…
- Kernel Principal Components Analysis
- performing / Performing Kernel Principal Components Analysis, How to do it…
- reference / Performing Kernel Principal Components Analysis
- kernels
- reference / How to do it…
L
- label encoding
- about / Label encoding, How to do it…
- Laplacian edge detector
- about / How to do it…
- URL / How to do it…
- Latent Dirichlet Allocation (LDA)
- about / How to do it…
- reference / How it works…
- learning curves
- about / Extracting learning curves
- extracting / Extracting learning curves, How to do it…
- learning vector quantization (LVQ) neural network / How to do it…
- lemmatization
- used, for converting text to base form / Converting text to its base form using lemmatization, How to do it…
- linear classifier
- building, Support Vector Machine (SVMs) used / Building a linear classifier using Support Vector Machine (SVMs), Getting ready, How to do it…
- linear regressor
- building / Building a linear regressor, Getting ready, How to do it…
- Local Binary Patterns Histograms
- used, for building face recognizer / Building a face recognizer using Local Binary Patterns Histogram, How to do it…
- about / Building a face recognizer using Local Binary Patterns Histogram
- reference / Building a face recognizer using Local Binary Patterns Histogram
- logistic regression classifier
M
- machine learning pipelines
- matplotlib
- URL / Introduction
- mean shift
- about / Building a Mean Shift clustering model
- reference / Building a Mean Shift clustering model
- mean shift clustering model
- building / Building a Mean Shift clustering model, How to do it…
- Mel Frequency Cepstral Coefficients (MFCC)
- model persistence
- achieving / Achieving model persistence, How to do it…
- movie recommendations
- generating / Generating movie recommendations, How to do it…
- music
- synthesizing / Synthesizing music, How to do it…
- URL / Synthesizing music
N
- natural language processing (NLP)
- about / Introduction
- Natural Language Toolkit (NLTK)
- about / Introduction
- references / Introduction
- Naïve Bayes classifier
- about / Building a Naive Bayes classifier
- building / How to do it…
- nearest neighbors
- finding / Finding the nearest neighbors, How to do it…
- about / Finding the nearest neighbors
- neural networks
- reference, for tutorial / Introduction
- used, for building optical character / Building an optical character recognizer using neural networks, How to do it…
- NeuroLab
- reference / Introduction
- neurons
- about / Introduction
- nonlinear classifier
- building, SVMs used / Building a nonlinear classifier using SVMs, How to do it…
- number of clusters
- estimating automatically, DBSCAN algorithm used / Automatically estimating the number of clusters using DBSCAN algorithm, How to do it…
- NumPy
- URL / Introduction
O
- object recognizer
- building / Building an object recognizer, How to do it…
- OpenCV
- about / Introduction
- URL / Introduction
- OpenCV-Python
- used, for operating on images / Operating on images using OpenCV-Python, How to do it…
- optical character recognizer
- building, neural networks used / Building an optical character recognizer using neural networks, How to do it…
- optimal hyperparameters
- searching / Finding optimal hyperparameters, How to do it…
- Ordinary Least Squares
- about / Getting ready
P
- pandas
- patterns
- finding, in stock market data / Finding patterns in stock market data, How to do it…
- Pearson correlation score
- computing / Computing the Pearson correlation score, How to do it…
- perceptron
- about / Building a perceptron
- building / Building a perceptron, How to do it…
- performance report
- extracting / Extracting the performance report, How to do it…
- pie charts
- drawing / Drawing pie charts, How to do it…
- Platt scaling
- about / How to do it…
- reference / How to do it…
- polynomial regressor
- building / Building a polynomial regressor, Getting ready, How to do it…
- predictive modeling
- about / Introduction
- Principal Components Analysis (PCA)
- performing / Performing Principal Components Analysis, How to do it…
- reference / Performing Principal Components Analysis
- pystruct
- reference / Getting ready
- Python packages
- NumPy / Introduction
- SciPy / Introduction
- scikit-learn / Introduction
- matplotlib / Introduction
R
- random forest regressor / Estimating bicycle demand distribution
- random forests
- recommendation engine
- about / Introduction
- recurrent neural network
- building, for sequential data analysis / Building a recurrent neural network for sequential data analysis, How to do it…
- reference / Building a recurrent neural network for sequential data analysis
- regression
- about / Building a linear regressor
- regression accuracy
- computing / Computing regression accuracy, How to do it…
- mean absolute error / Getting ready
- mean squared error / Getting ready
- median absolute error / Getting ready
- explained variance score / Getting ready
- R2 score / Getting ready
- regularization / Getting ready
- relative importance of features
- Ridge Regression / Getting ready
- ridge regressor
- building / Building a ridge regressor, Getting ready, How to do it…
S
- Scale Invariant Feature Transform (SIFT)
- about / Detecting SIFT feature points
- SciPy
- URL / Introduction
- sentiment analysis
- about / Analyzing the sentiment of a sentence
- performing / Analyzing the sentiment of a sentence, How to do it…, How it works…
- SIFT feature points
- detecting / Detecting SIFT feature points, How to do it…
- Silhouette Coefficient score
- simple classifier
- building / Building a simple classifier, How to do it…, There's more…
- single layer neural network
- building / Building a single layer neural network, How to do it…
- sobel filter
- about / How to do it…
- URL / How to do it…
- solid squares / Getting ready
- speech recognizer
- about / Introduction
- building / Building a speech recognizer, How to do it…
- Star feature detector
- building / Building a Star feature detector, How to do it…
- statistics
- extracting, from time series data / Extracting statistics from time series data, How to do it…
- stock market data
- patterns, finding in / Finding patterns in stock market data, How to do it…
- analyzing, hidden Markov models used / Analyzing stock market data using Hidden Markov Models, How to do it…
- support vector machine / How to do it…
- Support Vector Machine (SVMs)
- used, for building linear classifier / Building a linear classifier using Support Vector Machine (SVMs), Getting ready, How to do it…
- references, for tutorials / Building a linear classifier using Support Vector Machine (SVMs)
- used, for building nonlinear classifier / Building a nonlinear classifier using SVMs, How to do it…
T
- term frequency (TF) / Building a text classifier, How it works…
- text
- converting, to base form with lemmatization / Converting text to its base form using lemmatization, How to do it…
- dividing, chunking used / Dividing text using chunking, How to do it…
- patterns, identifying with topic modeling / Identifying patterns in text using topic modeling, How to do it…, How it works…
- text analysis
- about / Introduction
- text classifier
- building / Building a text classifier, How to do it…
- text data
- stemming / Stemming text data, How it works…
- tf-idf
- about / Building a text classifier
- URL / Building a text classifier
- time series data
- about / Introduction
- slicing / Slicing time series data, How to do it…
- operating on / Operating on time series data, How to do it…
- statistics, extracting from / Extracting statistics from time series data, How to do it…
- time series format
- data, transforming into / Transforming data into the time series format, How to do it…
- tokenization
- about / Preprocessing data using tokenization
- used, for preprocessing data / Preprocessing data using tokenization, How to do it…
- topic modeling
- about / Identifying patterns in text using topic modeling
- used, for identifying patterns in text / Identifying patterns in text using topic modeling, How to do it…, How it works…
- traffic
- estimating / Estimating traffic, How to do it…
U
- unsupervised learning
- about / Introduction
V
- validation curves
- extracting / Extracting validation curves, How to do it…
- vector quantization
- about / Compressing an image using vector quantization, How to do it…, Creating a vector quantizer
- used, for compressing image / Compressing an image using vector quantization, How to do it…
- reference / Compressing an image using vector quantization, How to do it…
- used, for creating features / Creating features using visual codebook and vector quantization, How to do it…
- vector quantizer
- creating / Creating a vector quantizer, How to do it…
- video
- capturing, from webcam / Capturing and processing video from a webcam, How to do it…
- processing, from webcam / Capturing and processing video from a webcam, How to do it…
- visual codebook
- used, for creating features / Creating features using visual codebook and vector quantization, How to do it…
- about / Creating features using visual codebook and vector quantization
- references / Creating features using visual codebook and vector quantization
W
- weak learners / Getting ready
- wholesale vendor and customers
- reference / Building a customer segmentation model