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
A Taste of Machine Learning
Working with Data in OpenCV
Dealing with Data Using Python's NumPy Package
Loading External Datasets in Python
Visualizing Data Using Matplotlib
Dealing with Data Using OpenCV's TrainData container
First Steps in Supervised Learning
Measuring Model Performance with Scoring Functions
Understanding the k-NN Algorithm
Using Regression Models to Predict Continuous Outcomes
Applying Lasso and Ridge Regression
Classifying Iris Species Using Logistic Regression
Representing Data and Engineering Features
Preprocessing Data
Reducing the Dimensionality of the Data
Representing Categorical Variables
Representing Text Features
Representing Images
Using Decision Trees to Make a Medical Diagnosis
Building Your First Decision Tree
Using Decision Trees to Diagnose Breast Cancer
Using Decision Trees for Regression
Detecting Pedestrians with Support Vector Machines
Implementing a Spam Filter with Bayesian Learning
Discovering Hidden Structures with Unsupervised Learning
Understanding k-Means Clustering
Compressing Color Images Using k-Means
Classifying Handwritten Digits Using k-Means
Implementing Agglomerative Hierarchical Clustering
Using Deep Learning to Classify Handwritten Digits
Understanding Perceptrons
Implementing a Multi-Layer Perceptron in OpenCV
Getting Acquainted with Deep Learning
Training an MLP in OpenCV to Classify Handwritten Digits
Training a Deep Neural Net to Classify Handwritten Digits Using Keras
Combining Different Algorithms Into an Ensemble
Understanding Ensemble Methods
Combining Decision Trees Into a Random Forest
Using Random Forests for Face Recognition
Implementing AdaBoost
Combining Different Models Into a Voting Classifier
Selecting the Right Model with Hyper-Parameter Tuning
Evaluating a Model
Understanding Cross-Validation, Bootstrapping, and McNemar's Test
Tuning Hyperparameters with Grid Search
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
