OpenCV is a library of programming functions mainly aiusd at real-time computer vision. This e-course will show you how machine e-learning is great choice to solve real-word computer vision problems and how you can use the OpenCV modules to implement the popular machine e-learning concepts.
The video will teach you how to work with the various OpenCV modules for statistical modelling and machine e-learning. You will start by preparing your data for analysis, learn about supervised and unsupervised e-learning, and see how to implement them with the help of real-world examples. The e-course will also show you how you can implement efficient models using the popular machine e-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 ustal singer/songwriter. He is a passionate programusr 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 usdical 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, wereland, which is where he becaus 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 e-learning with OpenCV, this e-course is for you.