Book Image

Machine Learning for OpenCV

By : Michael Beyeler
Book Image

Machine Learning for OpenCV

By: Michael Beyeler

Overview of this book

Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!

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Table of Contents (13 chapters)
Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
A Taste of Machine Learning
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
Summary
2
Working with Data in OpenCV and Python
Working with Data in OpenCV and Python
Understanding the machine learning workflow
Dealing with data using OpenCV and Python
Summary
3
First Steps in Supervised Learning
First Steps in Supervised Learning
Understanding supervised learning
Using classification models to predict class labels
Using regression models to predict continuous outcomes
Classifying iris species using logistic regression
Summary
4
Representing Data and Engineering Features
Representing Data and Engineering Features
Understanding feature engineering
Preprocessing data
Understanding dimensionality reduction
Representing categorical variables
Representing text features
Representing images
Summary
5
Using Decision Trees to Make a Medical Diagnosis
Using Decision Trees to Make a Medical Diagnosis
Understanding decision trees
Using decision trees to diagnose breast cancer
Using decision trees for regression
Summary
6
Detecting Pedestrians with Support Vector Machines
Detecting Pedestrians with Support Vector Machines
Understanding linear support vector machines
Dealing with nonlinear decision boundaries
Detecting pedestrians in the wild
Summary
7
Implementing a Spam Filter with Bayesian Learning
Implementing a Spam Filter with Bayesian Learning
Understanding Bayesian inference
Implementing your first Bayesian classifier
Classifying emails using the naive Bayes classifier
Summary
8
Discovering Hidden Structures with Unsupervised Learning
Discovering Hidden Structures with Unsupervised Learning
Understanding unsupervised learning
Understanding k-means clustering
Understanding expectation-maximization
Compressing color spaces using k-means
Classifying handwritten digits using k-means
Organizing clusters as a hierarchical tree
Summary
9
Using Deep Learning to Classify Handwritten Digits
Using Deep Learning to Classify Handwritten Digits
Understanding the McCulloch-Pitts neuron
Understanding the perceptron
Implementing your first perceptron
Understanding multilayer perceptrons
Getting acquainted with deep learning
Classifying handwritten digits
Summary
10
Combining Different Algorithms into an Ensemble
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
Summary
11
Selecting the Right Model with Hyperparameter Tuning
Selecting the Right Model with Hyperparameter Tuning
Evaluating a model
Understanding cross-validation
Estimating robustness using bootstrapping
Assessing the significance of our results
Tuning hyperparameters with grid search
Scoring models using different evaluation metrics
Chaining algorithms together to form a pipeline
Summary
12
Wrapping Up
Wrapping Up
Approaching a machine learning problem
Building your own estimator
Where to go from here?
Summary

Assessing the significance of our results

Assume for a moment that we implemented the cross-validation procedure for two versions of our k-NN classifier. The resulting test scores are-- 92.34% for Model A, and 92.73% for Model B. How do we know which model is better?

Following our logic introduced here, we might argue for Model B because it has a better test score. But what if the two models are not significantly different? These could have two underlying causes, which are both a consequence of the randomness of our testing procedure:

  • For all we know, Model B just got lucky. Perhaps we chose a really low k for our cross-validation procedure. Perhaps Model B ended up with a beneficial train-test split so that the model had no problem classifying the data. After all, we didn't run tens of thousands of iterations like in bootstrapping to make sure the result holds in general...
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