Machine Learning for OpenCV – Supervised Learning

Course details

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 availableon Github.

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