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Machine Learning for OpenCV.
Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. About This Book* Load, store, edit, and visualize data using OpenCV and Python* Grasp the fundamental concepts of classification, regression, and clustering* Understand, perform, and experi...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2017.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright; Credits; Foreword; About the Author; About the Reviewers; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: A Taste of Machine Learning; Getting started with machine learning; Problems that machine learning can solve; Getting started with Python; Getting started with OpenCV; Installation; Getting the latest code for this book; Getting to grips with Python's Anaconda distribution; Installing OpenCV in a conda environment; Verifying the installation; Getting a glimpse of OpenCV's ML module; Summary; Chapter 2: Working with Data in OpenCV and Python.
- Understanding the machine learning workflowDealing with data using OpenCV and Python; Starting a new IPython or Jupyter session; Dealing with data using Python's NumPy package; Importing NumPy; Understanding NumPy arrays; Accessing single array elements by indexing; Creating multidimensional arrays; Loading external datasets in Python; Visualizing the data using Matplotlib; Importing Matplotlib; Producing a simple plot; Visualizing data from an external dataset; Dealing with data using OpenCV's TrainData container in C++; Summary; Chapter 3: First Steps in Supervised Learning.
- Understanding supervised learningHaving a look at supervised learning in OpenCV; Measuring model performance with scoring functions; Scoring classifiers using accuracy, precision, and recall; Scoring regressors using mean squared error, explained variance, and R squared; Using classification models to predict class labels; Understanding the k-NN algorithm; Implementing k-NN in OpenCV; Generating the training data; Training the classifier; Predicting the label of a new data point; Using regression models to predict continuous outcomes; Understanding linear regression.
- Using linear regression to predict Boston housing pricesLoading the dataset; Training the model; Testing the model; Applying Lasso and ridge regression; Classifying iris species using logistic regression; Understanding logistic regression; Loading the training data; Making it a binary classification problem; Inspecting the data; Splitting the data into training and test sets; Training the classifier; Testing the classifier; Summary; Chapter 4: Representing Data and Engineering Features; Understanding feature engineering; Preprocessing data; Standardizing features; Normalizing features.
- Scaling features to a rangeBinarizing features; Handling the missing data; Understanding dimensionality reduction; Implementing Principal Component Analysis (PCA) in OpenCV; Implementing Independent Component Analysis (ICA); Implementing Non-negative Matrix Factorization (NMF); Representing categorical variables; Representing text features; Representing images; Using color spaces; Encoding images in RGB space; Encoding images in HSV and HLS space; Detecting corners in images; Using the Scale-Invariant Feature Transform (SIFT); Using Speeded Up Robust Features (SURF); Summary.