Computer Vision Specialization

Due to the COVID-19 outbreak our training courses will be taught via an online classroom.

With this training you will receive in-depth knowledge from industry professionals, test your skills with hands-on assignments & demos, and get access to valuable resources and tools.

Computer Vision has traditionally played a large role in applications such as image segmentation, compression, feature extraction, denoising and OCR. More recent developments in Deep Learning, and specifically in Convolutional Neural Networks, have been inspired by Computer Vision, thereby taking it to the next level. As part of a module on machine learning algorithms, this training will provide a thorough foundation of Computer Vision techniques.

The Computer Vision training is perfect for companies of all sizes that want to close the data gap and train their employees. You can follow the schedule below in our offices or contact us for a tailor-made program that meets your needs.

Close the Gap with this Computer Vision training

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Who should take this course?

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. 

Training Dates

This training consists of 1 lesson. Contact us for a tailored training. 

Description of the 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:

  • Computer Vision and its applications
  • Reading digital images and their representation
  • Transformation operations such as thresholding
  • Filters and convolutions
  • Gradients computation
  • Non-max-suppression
  • Canny Edge Detection
  • Scale Invariant Feature Transform (SIFT)
  • Computer Vision tools

After this training you receive a Certificate of Completion.