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• 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 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
  • 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
    • 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 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 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)
  • 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
• Chapter 5: Using Decision Trees to Make a Medical Diagnosis
  • Understanding decision trees
    • Building our first decision tree
      • 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
  • Understanding linear support vector machines
    • Learning optimal decision boundaries
    • Implementing our first support vector machine
      • 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 support vector machines
  • Detecting pedestrians in the wild
    • Obtaining the dataset
    • Taking a glimpse at the histogram of oriented gradients (HOG)
    • Generating negatives
    • Implementing the support vector machine
    • Bootstrapping the model
    • Detecting pedestrians in a larger image
    • Further improving the model
  • Summary
• Chapter 7: Implementing a Spam Filter with Bayesian Learning
  • Understanding Bayesian inference
    • Taking a short detour on 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
  • Understanding unsupervised learning
  • Understanding k-means clustering
    • Implementing our first k-means example
  • Understanding expectation-maximization
    • Implementing our own expectation-maximization solution
    • Knowing the limitations of expectation-maximization
      • First caveat: No guarantee of finding the global optimum
      • Second caveat: We must select the number of clusters beforehand
      • Third caveat: Cluster boundaries are linear
      • 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
  • Summary
• Chapter 9: Using Deep Learning to Classify Handwritten Digits
  • 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 multi-layer perceptrons with backpropagation
    • Implementing a multilayer perceptron 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 net using Keras
      • Preprocessing the MNIST dataset
      • Creating a convolutional neural network
      • Fitting the model
  • Summary
• Chapter 10: Combining Different Algorithms into an Ensemble
  • Understanding ensemble methods
    • Understanding averaging ensembles
      • Implementing a bagging classifier
      • Implementing a bagging regressor
    • Understanding boosting ensembles
      • 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
  • Summary
• Chapter 11: Selecting the Right Model with Hyperparameter Tuning
  • 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: Wrapping Up
  • 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
• Index
• Humble bundle_CDP.pdf
  • Table of Contents
  • Test
  • Index