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Machine Learning for OpenCV - Supervised Learning [Video]

By Michael Beyeler
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50+ new titles added every month on new and emerging tech
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What do you get with a Packt Subscription?
This book & 6500+ ebooks & video courses on 1000+ technologies
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50+ new titles added every month on new and emerging tech
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Mobile App with offline access
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Download this book in EPUB and PDF formats
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Stream this video
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What do you get with Audiobook?
Download a zip folder consisting of audio files (in MP3 Format) along with supplementary PDF
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Flashcards, Mock exams, Exam Tips, Practice Questions
Access these resources with our interactive certification platform
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About this video

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.

Publication date:
April 2018
Publisher
Packt
Duration
2 hours 39 minutes
ISBN
9781789347357

About the Author
  • 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.

    Browse publications by this author
Machine Learning for OpenCV - Supervised Learning [Video]
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