Machine Learning for OpenCV 4: intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn
Machine Learning for OpenCV 4, Second Edition will help the readers to implement and train machine learning algorithms with OpenCV 4 and scikit-learn in Python. By the end of this book, you will be able to build intelligent applications with OpenCV 4 using various optimization techniques for your ma...
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| Main Authors: | , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham ; Mumbai
Packt Publishing
2019
|
| Edition: | Second edition |
| Subjects: | |
| Online Access: | FHD01 |
| Summary: | Machine Learning for OpenCV 4, Second Edition will help the readers to implement and train machine learning algorithms with OpenCV 4 and scikit-learn in Python. By the end of this book, you will be able to build intelligent applications with OpenCV 4 using various optimization techniques for your machine learning algorithms |
| Item Description: | Representing text features |
| Physical Description: | 1 Online-Ressource (vii, 391 Seiten) |
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| 505 | 8 | |a Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Section 1: Fundamentals of Machine Learning and OpenCV; Chapter 1: A Taste of Machine Learning; Technical requirements; 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 | |
| 505 | 8 | |a Applications of machine learningWhat's new in OpenCV 4.0?; Summary; Chapter 2: Working with Data in OpenCV; Technical requirements; Understanding the machine learning workflow; Dealing 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 | |
| 505 | 8 | |a Dealing with data using OpenCV's TrainData container in C++Summary; Chapter 3: First Steps in Supervised Learning; Technical requirements; Understanding supervised learning; Having 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 | |
| 505 | 8 | |a Predicting the label of a new data pointUsing regression models to predict continuous outcomes; Understanding linear regression; Linear regression in OpenCV; Using linear regression to predict Boston housing prices; Loading 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 data into training and test sets; Training the classifier; Testing the classifier; Summary | |
| 505 | 8 | |a Chapter 4: Representing Data and Engineering FeaturesTechnical requirements; Understanding feature engineering; Preprocessing data; Standardizing features; Normalizing features; Scaling features to a range; Binarizing 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); Visualizing the dimensionality reduction using t-Distributed Stochastic Neighbor Embedding (t-SNE); Representing categorical variables | |
| 520 | |a Machine Learning for OpenCV 4, Second Edition will help the readers to implement and train machine learning algorithms with OpenCV 4 and scikit-learn in Python. By the end of this book, you will be able to build intelligent applications with OpenCV 4 using various optimization techniques for your machine learning algorithms | ||
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| contents | Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Section 1: Fundamentals of Machine Learning and OpenCV; Chapter 1: A Taste of Machine Learning; Technical requirements; 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 Applications of machine learningWhat's new in OpenCV 4.0?; Summary; Chapter 2: Working with Data in OpenCV; Technical requirements; Understanding the machine learning workflow; Dealing 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; Technical requirements; Understanding supervised learning; Having 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 pointUsing regression models to predict continuous outcomes; Understanding linear regression; Linear regression in OpenCV; Using linear regression to predict Boston housing prices; Loading 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 data into training and test sets; Training the classifier; Testing the classifier; Summary Chapter 4: Representing Data and Engineering FeaturesTechnical requirements; Understanding feature engineering; Preprocessing data; Standardizing features; Normalizing features; Scaling features to a range; Binarizing 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); Visualizing the dimensionality reduction using t-Distributed Stochastic Neighbor Embedding (t-SNE); Representing categorical variables |
| ctrlnum | (OCoLC)1145197194 (DE-599)BVBBV046631554 |
| edition | Second edition |
| format | Electronic eBook |
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| spelling | Sharma, Aditya Verfasser aut Machine Learning for OpenCV 4 intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn Aditya Sharma, Vishwesh Ravi Shrimali, Michael Beyeler Second edition Birmingham ; Mumbai Packt Publishing 2019 1 Online-Ressource (vii, 391 Seiten) txt rdacontent c rdamedia cr rdacarrier Representing text features Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Section 1: Fundamentals of Machine Learning and OpenCV; Chapter 1: A Taste of Machine Learning; Technical requirements; 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 Applications of machine learningWhat's new in OpenCV 4.0?; Summary; Chapter 2: Working with Data in OpenCV; Technical requirements; Understanding the machine learning workflow; Dealing 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; Technical requirements; Understanding supervised learning; Having 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 pointUsing regression models to predict continuous outcomes; Understanding linear regression; Linear regression in OpenCV; Using linear regression to predict Boston housing prices; Loading 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 data into training and test sets; Training the classifier; Testing the classifier; Summary Chapter 4: Representing Data and Engineering FeaturesTechnical requirements; Understanding feature engineering; Preprocessing data; Standardizing features; Normalizing features; Scaling features to a range; Binarizing 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); Visualizing the dimensionality reduction using t-Distributed Stochastic Neighbor Embedding (t-SNE); Representing categorical variables Machine Learning for OpenCV 4, Second Edition will help the readers to implement and train machine learning algorithms with OpenCV 4 and scikit-learn in Python. By the end of this book, you will be able to build intelligent applications with OpenCV 4 using various optimization techniques for your machine learning algorithms Machine learning Image processing OpenCV (Computer program language) Python (Computer program language) Image processing fast Machine learning fast OpenCV (Computer program language) fast Python (Computer program language) fast Shrimali, Vishwesh Ravi Verfasser aut Beyeler, Michael 1981- Verfasser (DE-588)1116574799 aut Erscheint auch als Druck-Ausgabe 978-1-78953-630-0 |
| spellingShingle | Sharma, Aditya Shrimali, Vishwesh Ravi Beyeler, Michael 1981- Machine Learning for OpenCV 4 intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Section 1: Fundamentals of Machine Learning and OpenCV; Chapter 1: A Taste of Machine Learning; Technical requirements; 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 Applications of machine learningWhat's new in OpenCV 4.0?; Summary; Chapter 2: Working with Data in OpenCV; Technical requirements; Understanding the machine learning workflow; Dealing 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; Technical requirements; Understanding supervised learning; Having 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 pointUsing regression models to predict continuous outcomes; Understanding linear regression; Linear regression in OpenCV; Using linear regression to predict Boston housing prices; Loading 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 data into training and test sets; Training the classifier; Testing the classifier; Summary Chapter 4: Representing Data and Engineering FeaturesTechnical requirements; Understanding feature engineering; Preprocessing data; Standardizing features; Normalizing features; Scaling features to a range; Binarizing 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); Visualizing the dimensionality reduction using t-Distributed Stochastic Neighbor Embedding (t-SNE); Representing categorical variables Machine learning Image processing OpenCV (Computer program language) Python (Computer program language) Image processing fast Machine learning fast OpenCV (Computer program language) fast Python (Computer program language) fast |
| title | Machine Learning for OpenCV 4 intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn |
| title_auth | Machine Learning for OpenCV 4 intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn |
| title_exact_search | Machine Learning for OpenCV 4 intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn |
| title_exact_search_txtP | Machine Learning for OpenCV 4 intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn |
| title_full | Machine Learning for OpenCV 4 intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn Aditya Sharma, Vishwesh Ravi Shrimali, Michael Beyeler |
| title_fullStr | Machine Learning for OpenCV 4 intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn Aditya Sharma, Vishwesh Ravi Shrimali, Michael Beyeler |
| title_full_unstemmed | Machine Learning for OpenCV 4 intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn Aditya Sharma, Vishwesh Ravi Shrimali, Michael Beyeler |
| title_short | Machine Learning for OpenCV 4 |
| title_sort | machine learning for opencv 4 intelligent algorithms for building image processing apps using opencv 4 python and scikit learn |
| title_sub | intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn |
| topic | Machine learning Image processing OpenCV (Computer program language) Python (Computer program language) Image processing fast Machine learning fast OpenCV (Computer program language) fast Python (Computer program language) fast |
| topic_facet | Machine learning Image processing OpenCV (Computer program language) Python (Computer program language) |
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