Datasets often have many more features than we could possibly process. For example, let's say our job was to predict a country's poverty rate. We would probably start by matching a country's name with its poverty rate, but that would not help us predict the poverty rate of a new country. So we start thinking about possible causes of poverty. But how many possible causes of poverty are there? Factors might include a country's economy, lack of education, high divorce rate, overpopulation, and so on. If each one of these causes was a feature used to help predict the poverty rate, we would end up with a countless number of features. If you're a mathematician, you might think of these features as axes in a high-dimensional space, and every country's poverty rate is then a single point in this high-dimensional space...
Machine Learning for OpenCV
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Machine Learning for OpenCV
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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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Current Title:
Machine Learning for OpenCV
Table of Contents (13 chapters)
Preface
Free Chapter
A Taste of Machine Learning
Working with Data in OpenCV and Python
First Steps in Supervised Learning
Representing Data and Engineering Features
Using Decision Trees to Make a Medical Diagnosis
Detecting Pedestrians with Support Vector Machines
Implementing a Spam Filter with Bayesian Learning
Discovering Hidden Structures with Unsupervised Learning
Using Deep Learning to Classify Handwritten Digits
Combining Different Algorithms into an Ensemble
Selecting the Right Model with Hyperparameter Tuning
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