«The model seems to work, but in the real pipeline everything falls apart»..
«I either build filters and descriptors, or throw everything straight into a neural net — and I don’t understand which is better…»..
«I need object tracking / video analytics / 3D — but I don’t have end-to-end experience»..
Sound familiar? You’re already working or want to grow in the Computer Vision direction and run into similar difficulties, while most solutions remain stuck at the demo level? Maybe you’ve simply “stalled” at a certain point in your career growth and want a boost?
The Computer Vision course is about building engineering thinking and solving your real problems now, not a set of fragmented techniques.
Who is this course for?
Software Developers — who want to move beyond standard applications and start building Computer Vision solutions.
Embedded Developers — who need to add intelligent recognition systems to work with devices and sensors.
Research Engineers — who plan to grow a career in R&D, combining theory with hands-on experiments.
Data Scientists / ML Engineers — who aim to deepen data analysis by working not only with tables, but also with visual information.
Experienced Python developers who plan to change their specialization and enter the Computer Vision field with mentor support.
Topics to be covered
Topic 1. Models of Representation and Transformation of Digital Images.
Lesson 1. Raster Digital Images.
Lesson 2. Vector Digital Images.
Homework 1. Raster and Vector Digital Images.
Topic 2.Filtering and Enhancing the Quality of Digital Images.
Lesson 3. Digital Image Filtering.
Lesson 4. Image Quality Enhancement.
Homework 2. Filtering and Enhancing the Quality of Digital Images.
Topic 3. Image Feature Extraction and Their Practical Use.
Lesson 5. Digital Image Descriptor. Object Tracking Technology
Homework 3. Technologies for Comparing Digital Images and Object Tracking.
Lesson 6. Machine Learning — Clustering and Classification.
Homework 4. Clustering and Classification Technologies in Computer Vision.
Lesson 7. Machine Learning — Identification.
Homework 5. Identification Technologies in Computer Vision.
Class 8. Fundamentals of artificial neural networks.
Class 9. Application of artificial neural networks.
Homework 6. Implementation of deep learning methods in Computer Vision.
Class 10. Reconstruction of 3D images.
Homework 7. Technologies of 3D image reconstruction
After the course you will:
- Develop your own solutions for digital image processing and build a portfolio of case studies
- Learn to perform image segmentation and recognize objects in photos and videos
- Implement object search and tracking in real-time video streams
- Understand 3D scene reconstruction and navigation and be able to apply these approaches in practice
- Overcome the “just theory” barrier — start applying Computer Vision in real projects
- Gain confidence in using Computer Vision for applied business and R&D tasks
FAQ
Do I need prior experience in Computer Vision?
No. The course is designed for developers with basic knowledge of Python and mathematics and gradually leads to the applied use of Computer Vision.
What is the format of the course?
Online classes according to the schedule (Mon/Wed 19:00), practical homework assignments, and materials for self-study.
What tools and approaches are used?
Modern Computer Vision methods, machine learning, and deep learning approaches applied in real R&D and product tasks.
Which roles will benefit from this course?
Software Developers, Embedded Engineers, Research Engineers, Data Scientists, and Python developers who want to work with visual data.
Will there be support from the instructor?
Yes. You will work with an expert who has real experience implementing Computer Vision in projects and can help clarify complex concepts.
Payment in installments without commission and interest is available from PrivatBank
