Participants in this training typically have a background in a quantitative subject, analytics or are junior/medior data scientists, who have the ambition to dive deeper into specific machine learning algorithms and techniques. In order to get most value out of the training, you should have basic experience with programming in Python. Furthermore, basic knowledge of mathematics is desired, specifically on calculus (derivatives). Some experience in dealing with git repositories can come in handy for obtaining the course materials.
After the training you receive a Certificate of Completion.
This training consists of 1 lesson. You can join on one of these dates in our office or contact us for a tailored training:
This training starts off by giving a good introduction on what Computer Vision is, its applications and its relation to the human visual system. We then move to the theoretical part in which we learn how digital images are represented and processed, using basic operations such as thresholding, convolutions and filters.
These concepts will come together in the second part of the training, in which we discuss Canny Edge Detection and its relation to concepts such as Gaussian kernels, gradient computation, non-max-suppression and hysteresis. We then dive into Scale Invariant Feature Transformation (SIFT), a powerful technique for identifying key feature points, often used in image stitching, and conclude by providing a list of commonly used Computer Vision tools and resources.
The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:
After this training you receive a Certificate of Completion.
Are you interested in the Computer Vision Specialization training or do you have questions? Fill out the form and we will contact you personally.