MACHINE LEARNING FOR OPENCV 4 : intelligent algorithms for building image processing apps... using opencv 4, python, and scikit-learn, 2nd edit.

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 learning
	What'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 point
	Using 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 Features
	Technical 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
	Representing text features
	Representing images
		Using color spaces
			Encoding images in the RGB space
			Encoding images in the HSV and HLS space
		Detecting corners in images
		Using the star detector and BRIEF descriptor
		Using Oriented FAST and Rotated BRIEF (ORB)
	Summary
Section 2: Operations with OpenCV
Chapter 5: Using Decision Trees to Make a Medical Diagnosis
	Technical requirements
	Understanding decision trees
		Building our first decision tree
			Generating new data
			Understanding the task by understanding the data
			Preprocessing the data
			Constructing the tree
		Visualizing a trained decision tree
		Investigating the inner workings of a decision tree
		Rating the importance of features
		Understanding the decision rules
		Controlling the complexity of decision trees
	Using decision trees to diagnose breast cancer
		Loading the dataset
		Building the decision tree
	Using decision trees for regression
	Summary
Chapter 6: Detecting Pedestrians with Support Vector Machines
	Technical requirement
	Understanding linear SVMs
		Learning optimal decision boundaries
		Implementing our first SVM
			Generating the dataset
			Visualizing the dataset
			Preprocessing the dataset
			Building the support vector machine
			Visualizing the decision boundary
	Dealing with nonlinear decision boundaries
		Understanding the kernel trick
		Knowing our kernels
		Implementing nonlinear SVMs
	Detecting pedestrians in the wild
		Obtaining the dataset
		Taking a glimpse at the histogram of oriented gradients (HOG)
		Generating negatives
		Implementing the SVM
		Bootstrapping the model
		Detecting pedestrians in a larger image
		Further improving the model
		Multiclass classification using SVMs
			About the data
			Attribute information
	Summary
Chapter 7: Implementing a Spam Filter with Bayesian Learning
	Technical requirements
	Understanding Bayesian inference
		Taking a short detour through probability theory
		Understanding Bayes' theorem
		Understanding the Naive Bayes classifier
	Implementing your first Bayesian classifier
		Creating a toy dataset
		Classifying the data with a normal Bayes classifier
		Classifying the data with a Naive Bayes classifier
		Visualizing conditional probabilities
	Classifying emails using the Naive Bayes classifier
		Loading the dataset
		Building a data matrix using pandas
		Preprocessing the data
		Training a normal Bayes classifier
		Training on the full dataset
		Using n-grams to improve the result
		Using TF-IDF to improve the result
	Summary
Chapter 8: Discovering Hidden Structures with Unsupervised Learning
	Technical requirements
	Understanding unsupervised learning
	Understanding k-means clustering
		Implementing our first k-means example
	Understanding expectation-maximization
		Implementing our expectation-maximization solution
		Knowing the limitations of expectation-maximization
			The first caveat – no guarantee of finding the global optimum
			The second caveat – we must select the number of clusters beforehand
			The third caveat – cluster boundaries are linear
			The fourth caveat – k-means is slow for a large number of samples
	Compressing color spaces using k-means
		Visualizing the true-color palette
		Reducing the color palette using k-means
	Classifying handwritten digits using k-means
		Loading the dataset
		Running k-means
	Organizing clusters as a hierarchical tree
		Understanding hierarchical clustering
		Implementing agglomerative hierarchical clustering
		Comparing clustering algorithms
	Summary
Section 3: Advanced Machine Learning with OpenCV
Chapter 9: Using Deep Learning to Classify Handwritten Digits
	Technical requirements
	Understanding the McCulloch-Pitts neuron
	Understanding the perceptron
	Implementing your first perceptron
		Generating a toy dataset
		Fitting the perceptron to data
		Evaluating the perceptron classifier
		Applying the perceptron to data that is not linearly separable
	Understanding multilayer perceptrons
		Understanding gradient descent
		Training MLPs with backpropagation
		Implementing a MLP in OpenCV
			Preprocessing the data
			Creating an MLP classifier in OpenCV
			Customizing the MLP classifier
			Training and testing the MLP classifier
	Getting acquainted with deep learning
		Getting acquainted with Keras
	Classifying handwritten digits
		Loading the MNIST dataset
		Preprocessing the MNIST dataset
		Training an MLP using OpenCV
		Training a deep neural network using Keras
			Preprocessing the MNIST dataset
			Creating a convolutional neural network
			Model summary
			Fitting the model
	Summary
Chapter 10: Ensemble Methods for Classification
	Technical requirements
	Understanding ensemble methods
		Understanding averaging ensembles
			Implementing a bagging classifier
			Implementing a bagging regressor
		Understanding boosting ensembles
			Weak learners
			Implementing a boosting classifier
			Implementing a boosting regressor
		Understanding stacking ensembles
	Combining decision trees into a random forest
		Understanding the shortcomings of decision trees
		Implementing our first random forest
		Implementing a random forest with scikit-learn
		Implementing extremely randomized trees
	Using random forests for face recognition
		Loading the dataset
		Preprocessing the dataset
		Training and testing the random forest
	Implementing AdaBoost
		Implementing AdaBoost in OpenCV
		Implementing AdaBoost in scikit-learn
	Combining different models into a voting classifier
		Understanding different voting schemes
		Implementing a voting classifier
		Plurality
	Summary
Chapter 11: Selecting the Right Model with Hyperparameter Tuning
	Technical requirements
	Evaluating a model
		Evaluating a model the wrong way
		Evaluating a model in the right way
		Selecting the best model
	Understanding cross-validation
		Manually implementing cross-validation in OpenCV
		Using scikit-learn for k-fold cross-validation
		Implementing leave-one-out cross-validation
	Estimating robustness using bootstrapping
		Manually implementing bootstrapping in OpenCV
	Assessing the significance of our results
		Implementing Student's t-test
		Implementing McNemar's test
	Tuning hyperparameters with grid search
		Implementing a simple grid search
		Understanding the value of a validation set
		Combining grid search with cross-validation
		Combining grid search with nested cross-validation
	Scoring models using different evaluation metrics
		Choosing the right classification metric
		Choosing the right regression metric
	Chaining algorithms together to form a pipeline
		Implementing pipelines in scikit-learn
		Using pipelines in grid searches
	Summary
Chapter 12: Using OpenVINO with OpenCV
	Technical requirements
	Introduction to OpenVINO
	OpenVINO toolkit installation
		OpenVINO components
	Interactive face detection demo
	Using OpenVINO Inference Engine with OpenCV
	Using OpenVINO Model Zoo with OpenCV
	Image classification using OpenCV with OpenVINO Inference Engine
		Image classification using OpenVINO
		Image classification using OpenCV with OpenVINO
	Summary
Chapter 13: Conclusion
	Technical requirements
	Approaching a machine learning problem
	Building your own estimator
		Writing your own OpenCV-based classifier in C++
		Writing your own scikit-learn-based classifier in Python
	Where to go from here
	Summary
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