Machine Learning for OpenCV 3 - Advanced Methods

Course details

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

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.

About the Author :

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.