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Before you begin your journey into the exciting world of
Computer Vision, Deep Learning, and AI, you need to
become an expert at using the world’s largest resource of
Computer Vision, the OpenCV library. This free OpenCV course will teach
you how to manipulate images and videos, and detect
objects and faces, among other exciting topics in just
about 3 hours.
Start your AI journey by learning the fundamentals of Image Processing and Computer Vision through 21 modules, video instructions, code explanations, and example applications.
Available in Python
Start solving Computer Vision problems using Deep Learning techniques and the PyTorch framework. Dive into the architecture of Neural Networks, and learn how to train and deploy them on the cloud.
Dive deep into Stable Diffusion. Learn all the techniques of generating images, fine-tuning Stable Diffusion on your own images and even training a GPT language model.
Gain in-depth knowledge about Neural
Networks, prepare datasets and study
DeepNet architectures used for solving
various Computer Vision problems.
Build a solid understanding of OpenCV tools used for Image Processing, Computer Vision, and Video Processing, and lay a strong foundation for solving Computer Vision problems.
Available in Python & C++
Build systems and applications using advanced Computer Vision and Deep Learning techniques, and understand deployment using cloud-based services.
To get the most from our courses, you should possess a working knowledge of Python or a similar programming language. For the courses offered in C++, you should have a basic proficiency in C++.
Aside from the programming experience mentioned above, the series of courses are designed to take you from the fundamentals in Image Processing and Computer Vision through more advanced topics in Deep Learning. If you are looking to jump in directly to our Deep Learning courses, then you should have a good understanding of the foundational material in Image Processing and Computer Vision.
Upon finishing a course, you will be awarded a certificate of completion from OpenCV.org. To qualify for the certificate, you must complete all graded quizzes, assignments, and projects, obtaining a score of at least 50% within six months of enrollment. If your score exceeds 70%, you will be granted an Honor Certificate.
The time it takes to complete a course depends on the number of hours you can dedicate weekly. Based on our observations, students typically finish the courses in the following timeframes:
Mastering OpenCV For Computer Vision: Approximately 2-4 weeksFundamentals Of Computer Vision & Image Processing: Roughly 3 monthsAdvanced Computer Vision and Deep Learning Applications: Around 3 monthsDeep Learning With PyTorch: About 4-5 monthsDeep Learning With TensorFlow & Keras: Approximately 4-5 months
Please note that taking the time to fully comprehend the course material is essential rather than rushing through it. This will ensure a deeper understanding and better retention of the content.
If you're looking for the most comprehensive option, the CV Master Bundle offers the complete set of courses provided by OpenCV.org. However, if the CV Master Bundle is too extensive or costly for your needs, we recommend the CV DL Starter. This bundle equips you with a strong foundation in both traditional computer vision and modern deep learning approaches.
OpenCV University is a trustworthy partner of learners across the globe.
Courses are (a little) oversubscribed and we apologize for your enrollment delay. As an apology, you will receive a 20% discount on all waitlist course purchases. Current wait time will be sent to you in the confirmation email. Thank you!