OpenCV is a library of programming functions mainly aimed at real-time computer vision. This course will show you how machine learning is great choice to solve real-word computer vision problems and how you can use the OpenCV modules to implement the popular machine learning concepts.
The video will teach you how to work with the various OpenCV modules for statistical modelling and machine learning. You will start by preparing your data for analysis, learn about supervised and unsupervised learning, and see how to implement them with the help of real-world examples. The course will also show you how you can implement efficient models using the popular machine learning techniques such as classification, regression, decision trees, K-nearest neighbors, boosting, and neural networks with the aid of C++ and OpenCV.
About The Author
Joe Minichino is a computer vision engineer for Hoolux Medical by day and a developer of the NoSQL database LokiJS by night. On weekends, he is a heavy metal singer/songwriter. He is a passionate programmer who is immensely curious about programming languages and technologies and constantly experiments with them. At Hoolux, Joe leads the development of an Android computer vision-based advertising platform for the medical industry.
Born and raised in Varese, Lombardy, Italy, and coming from a humanistic background in philosophy (at Milan's UniversitàStatale), Joe has spent his last 11 years living in Cork, Ireland, which is where he became a computer science graduate at the Cork Institute of Technology.
Joe is also the author of Learning OpenCV 3 Computer Vision with Python, Second Edition also for Packt Publishing.
Who is the target audience?
- If you have a basic working knowledge of computer vision and OpenCV, and want to perform machine learning with OpenCV, this course is for you.
