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Machine Learning for OpenCV - Supervised Learning , published by Packt
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Machine Learning for OpenCV - Supervised Learning [Video]

This is the code repository for Machine Learning for OpenCV - Supervised Learning [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.

About the Video Course

Are you trying to build your own OpenCV system and you are facing any problems? Want to understand the key features of Machine learning?

If yes, then this is the course you’re looking for!

We will start by discussing all the problems involved in creating a OpenCV system. Then learn to fit data using tools of our choice. Also we will exploring various algorithms such as decision trees, support vector machines, and learn how to combine them with other OpenCV functionality. Finally we will build a machine learning system that can make a medical diagnosis.

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

What You Will Learn

  • Explore and make effective use of OpenCV's Machine Learning module.
  • Master linear regression and regularization techniques.
  • Classify objects such as flower species and pedestrians.
  • Creatively build decision trees in OpenCV.
  • Explore the effective use of support vector machines, boosted decision trees, and random forests.
  • Learn to visualize data with OpenCV and Python.

Instructions and Navigation

Assumed Knowledge

To fully benefit from the coverage included in this course, you will need:
This video course is for those Python programmers who are already familiar with OpenCV.

Technical Requirements

This course has the following software requirements:
Python's Anaconda distribution, based on Python 3.5 or higher OpenCV 3.1 or higher Some supporting packages

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