Book Image

Machine Learning for OpenCV - Supervised Learning [Video]

By : Michael Beyeler
Book Image

Machine Learning for OpenCV - Supervised Learning [Video]

By: Michael Beyeler

Overview of this book

<p>Computer vision is one of today's most exciting application fields of Machine Learning, From self-driving cars to Medical diagnosis, this has been widely used in various domains.</p> <p>This course will take you right from the essential concepts of statistical learning to help you with various algorithms to implement it with other OpenCV tasks.</p> <p>The course will also guide you through creating custom graphs and visualizations, and show you how to go from the raw data to beautiful visualizations. We will also build a machine learning system that can make a medical diagnosis.</p> <p>By the end of this course, you will be ready create your own ML system and will also be able to take on your own machine learning problems.</p> <p>All the code and supporting files for this course are available on Github at <a style="color: #fa8d11;" href="https://github.com/PacktPublishing/Machine-Learning-for-OpenCV-Supervised-Learning" target="blank">https://github.com/PacktPublishing/Machine-Learning-for-OpenCV-Supervised-Learning</a></p> <h2>Style and Approach</h2> <p>This course walks you through the key elements of OpenCV and its powerful Machine Learning classes while demonstrating how to get to grips with a range of models.</p>
Table of Contents (6 chapters)
Chapter 5
Using Decision Trees to Make a Medical Diagnosis
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Section 4
Using Decision Trees for Regression
In this video, first we will learn to use decision tree for regression. Then we will use 'MSE' criterion to build two trees. Also, we will make an attempt to use the decision tree like a linear regressor. - Add noise to the data points using NumPy's random number generator - Create 100 x values between 0 and 5, and calculate the corresponding sin values - Use ‘MSE’ criterion to build two trees; one with a depth of 2 and one with a depth of 5