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

More Information
  • 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

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.

  • 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


Michael Beyeler

Michael Beyeler is a Postdoctoral Fellow at the University of Washington in Seattle. His work lies at the intersection of neuroscience, computer vision, and machine learning. Michael is the author of two Packt books: OpenCV with Python Blueprints (2015) and Machine Learning for OpenCV (2017). He is an active contributor to several open-source software projects and has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android. His technical blog can be found at www.askaswiss.com.