This is the Jupyter notebook version of the following book:
Michael Beyeler
Machine Learning for OpenCV: A practical introduction to the world of machine learning and image processing using OpenCV and Python
Table of Contents
Preface
Foreword by Ariel Rokem
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A Taste of Machine Learning
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Working with Data in OpenCV
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Dealing with Data Using Python's NumPy Package
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Loading External Datasets in Python
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Visualizing Data Using Matplotlib
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Dealing with Data Using OpenCV's TrainData container
First Steps in Supervised Learning
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Measuring Model Performance with Scoring Functions
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Understanding the k-NN Algorithm
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Using Regression Models to Predict Continuous Outcomes
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Applying Lasso and Ridge Regression
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Classifying Iris Species Using Logistic Regression
Representing Data and Engineering Features
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Preprocessing Data
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Reducing the Dimensionality of the Data
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Representing Categorical Variables
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Representing Text Features
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Representing Images
Using Decision Trees to Make a Medical Diagnosis
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Building Your First Decision Tree
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Using Decision Trees to Diagnose Breast Cancer
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Using Decision Trees for Regression
Detecting Pedestrians with Support Vector Machines
Implementing a Spam Filter with Bayesian Learning
Discovering Hidden Structures with Unsupervised Learning
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Understanding k-Means Clustering
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Compressing Color Images Using k-Means
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Classifying Handwritten Digits Using k-Means
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Implementing Agglomerative Hierarchical Clustering
Using Deep Learning to Classify Handwritten Digits
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Understanding Perceptrons
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Implementing a Multi-Layer Perceptron in OpenCV
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Getting Acquainted with Deep Learning
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Training an MLP in OpenCV to Classify Handwritten Digits
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Training a Deep Neural Net to Classify Handwritten Digits Using Keras
Combining Different Algorithms Into an Ensemble
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Understanding Ensemble Methods
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Combining Decision Trees Into a Random Forest
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Using Random Forests for Face Recognition
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Implementing AdaBoost
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Combining Different Models Into a Voting Classifier
Selecting the Right Model with Hyper-Parameter Tuning
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Evaluating a Model
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Understanding Cross-Validation, Bootstrapping, and McNemar's Test
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Tuning Hyperparameters with Grid Search
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Chaining Algorithms Together to Form a Pipeline
Wrapping Up
链接：
https://github.com/mbeyeler/opencv-machine-learning
原文链接：
https://m.weibo.cn/1402400261/4132029312680424
