OpenCV Bootcamp

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.

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Course Code
OCVBC

Type
Preparatory

Available in
Python

Price
Free*

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OpenCV Bootcamp

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.

Course Code
OCVBC

Type
Preparatory

Available in
Python

Price
Free*

Enroll for Free

Official OpenCV Certification

14 Modules

Videos

Quizzes

3Hrs Content

Colab Notebooks

What's covered in this course?

Tool Kit

Testimonials

Courses Offered

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GENAI

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.

Available in Python

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$299

$299 $209

Frequently Asked Questions

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Yes, our courses are designed to accommodate learners with varying levels of experience. All that is required is a basic understanding of at least one programming language (Python is preferable but not mandatory). We will walk you through the fundamental concepts, providing step-by-step guidance.

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.

Our team will be available in the course forum to address your questions and provide guidance as needed. We encourage students to post questions on the forum, as other students can often help. In fact, you will find that answering questions for other students will facilitate your own learning. However, rest assured that our instructors monitor the course forum, and we strive to respond within 24 hours. In the end, it’s a highly collaborative environment that allows everyone involved the opportunity to learn.
The course content features a combination of theoretical explanations and practical code demonstrations delivered through both text and video formats. Quizzes, assignments, and projects of varying difficulty levels are also included. Students have the option to choose the assignments or projects they wish to work on, with each carrying a point value. We select the best "m" out of "n" assignments/projects to accommodate individual interests.

This approach allows students to focus on topics that pique their interest, enabling them to delve deeper by completing relevant assignments/projects while having the flexibility to skip topics they find less appealing.
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Yes, you can share an account with a friend. However, please note that if you both qualify for a certificate, we can only issue one certificate per account.
Upon purchasing the course, you should have received an enrollment email. If it's not in your inbox, please check your spam folder. If you still cannot locate the email, reach out to us at [email protected] for assistance.
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Absolutely. If you're not satisfied with a course, simply send us an email ([email protected]) within 30 days of the course enrollment date, and we will issue a full refund without any questions asked.
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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 weeks
Fundamentals Of Computer Vision & Image Processing: Roughly 3 months
Advanced Computer Vision and Deep Learning Applications: Around 3 months
Deep Learning With PyTorch: About 4-5 months
Deep 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.

We offer lifetime access to our courses, allowing you to use them as reference materials long after completion. Please note that for all courses except OpenCV Essentials, we provide online labs with Jupyter notebooks integrated into the course platform for assignment submissions.
No, you have the flexibility to decide when to begin each course. To receive a certificate, you must complete each individual course within 6 months of enrolling in it. The 6-month timeframe applies separately to each course within the bundle, starting from the enrollment date of each specific course.
Yes, you will have lifetime access to the certificate on the course platform. You will get a digital certificate after successfully completing the course and you can share this certificate on LinkedIn, Facebook and other platforms.
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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.

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