Machine Learning for OpenCV - Advanced Methods and Deep Learning [Video]

More Information
Learn
  • Implement a Naïve Bayes classifier
  • Discover hidden structures in your data using k-means clustering
  • Implement k-means clustering and Expectation Maximization in OpenCV
  • Implement a simple multi-layer perceptron in OpenCV
  • Train and tweak neural networks
  • Build an ensemble classifier from decision trees in OpenCV
  • Combine different algorithms into a simple majority-vote classifier
  • Learn to tweak the hyperparameters of a model
About

Computer vision is one of today's most exciting application fields of Machine Learning, From self-driving cars to medical diagnosis, computer vision has been widely used in various domains.

This course will cover essential concepts such as classifiers and clustering and will also help you get acquainted with neural networks and Deep Learning to address real-world problems.

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

The course will also guide you through creating custom graphs and visualizations, and show you how to go from raw data to beautiful visualizations. By the end of this course, you will be ready to create your own ML system and will also be able to take on your own machine learning problems.

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 advanced concepts of bootstrapping, boosting, voting, and bagging
  • Evaluate, compare, and choose the right algorithm for any task
  • Load, store, edit and visualize data using OpenCV and Python
Course Length 2 hours 25 minutes
ISBN 9781789340525
Date Of Publication 20 May 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.