Machine Learning for OpenCV - Supervised Learning [Video]

More Information
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.
About

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.

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.

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.

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.

All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Machine-Learning-for-OpenCV-Supervised-Learning

Style and Approach

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.

Features
  • Understand, perform, and experiment with Machine Learning techniques using this easy-to-follow guide
  • Grasp the fundamental concepts of classification, regression, and clustering
  • Evaluate, compare, and choose the right algorithm for any task 
  • Load, store, edit, and visualize data using OpenCV and Python
Course Length 2 hours 39 minutes
ISBN 9781789347357
Date Of Publication 29 Apr 2018

Authors

Michael Beyeler

Michael Beyeler is a postdoctoral fellow in neuroengineering and data science at the University of Washington, where he is working on computational models of bionic vision in order to improve the perceptual experience of blind patients implanted with a retinal prosthesis (bionic eye). His work lies at the intersection of neuroscience, computer engineering, computer vision, and machine learning. He is also an active contributor to several open source software projects, and has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android. Michael received a PhD in computer science from the University of California, Irvine, and an MSc in biomedical engineering and a BSc in electrical engineering from ETH Zurich, Switzerland.