Computer vision is a subfield of Artificial intelligence that makes computing systems derive interpretable information and based on its assessments it provides valuable recommendations or otherwise takes actions of its own. Computer vision authorizes computers to observe and apprehend. The fundamental motive behind the invention of this field is that the data generated nowadays is enormous. There are hundreds of thousands of images shared every day online. As these images can comprise photos and videos so they can also carry data obtained from optical sensors or thermal sensors and other sources as well.
Computer vision is a replica of human vision but computer vision has to carry out all these functions quickly with algorithms. The major feature that surpasses human abilities is that it can detect minute and indistinguishable errors every second.
Statistics show that the market will keep growing and was expected to reach approximately USD 48.6 billion by 2022 and it proved to be of more worth than this by 2023.
# | Course Name | University/Organization | Ratings | Duration |
1. | Introduction to Computer Vision and Image Processing | IBM | ★★★★☆ 4.4 | 21 Hours |
2. | Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs | Udemy | ★★★★☆ 4.3 | 11 Hours |
3. | Convolutional Neural Networks | DeepLearning.AI | ★★★★★ 4.9 | 36 Hour |
4. | Computer Vision Executive Education Program | Carnegie Mellon University | — | 100 Hours |
5. | Become a Computer Vision Expert | Udacity | — | 180 Hours |
6. | Modern Computer Vision™ PyTorch, Tensorflow2 Keras & OpenCV4 | Udemy | ★★★★★ 4.5 | 28 Hours |
7. | Computer Vision for Engineering and Science Specialization | MathWorks | ★★★★★ 5.0 | 36 Hours |
8. | Python for Computer Vision with OpenCV and Deep Learning | Udemy | ★★★★★ 4.6 | 14 Hours |
9. | Python Project: pillow, tesseract, and opencv | University of Michigan | ★★★★☆ 4.0 | 19 Hours |
10. | Deep Learning: Advanced Computer Vision (GANs, SSD, +More!) | Udemy | ★★★★★ 4.7 | 16 Hours |
11. | Advanced Computer Vision with TensorFlow | DeepLearning.AI | ★★★★★ 4.8 | 19 Hours |
Best + Free Computer Vision Courses & Certification Programs
Introduction to Computer Vision and Image Processing
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- IBM via Coursera
- 46,257+ already enrolled!
- ★★★★☆ (928 Ratings)
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Content | Engagement | Practice | Career Benefit |
Good ★★★★☆ |
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Fair ★★★☆☆ |
Fair ★★★☆☆ |
This course explains the wide variety of applications of computer vision in industries and other governmental organizations, such as augmented reality, cancer detection, road conditioning monitoring, reading barcodes, product assembly, and many more. For image processing, this course teaches you to employ Pillow, Python, and others such as OpenCV.
- The reason behind the selection of this course is that this course helps the participant in getting hands-on experience in object detection. Practical exercises and labs are also included in this course. Labs incorporate Jupyter and CV Studio.
- This course is for beginners and a fresher willing to understand what computer vision is.
Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs
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- Hadelin de Ponteves via Udemy
- 46,842+ already enrolled!
- ★★★★☆ (6,447 Ratings)
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Content | Engagement | Practice | Career Benefit |
Excellent ★★★★★ |
Fair ★★★☆☆ |
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This course not only provides knowledge regarding computer vision and how to use it but also allows maximizing its utilization.
- The reason why we chose this course is that the core objective of the course is to help the participants not only understand how the most popular computer vision methods work but also to learn and apply them practically.
- This course is for every individual looking to develop an insight into computer vision.
Convolutional Neural Networks
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- DeepLearning.AI via Coursera
- 452,445+ already enrolled!
- ★★★★★ (41,406 Ratings)
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Content | Engagement | Practice | Career Benefit |
Excellent ★★★★★ |
Good ★★★★☆ |
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In this course, you will learn how to develop a convolutional neural network. This course also teaches you about residual networks, integration of the convolutional network with visual detection and recognition tasks, production and deployment of these algorithms to process 2D or 3D A/V data, and much more.
- This course is selected because in this course, apart from technical development, you will also get insights into the challenges and ramifications of deep learning to help you develop your skills according to advanced AI tech. This course not only helps you to enhance your knowledge and skills but also to apply it at your workplace and give a boost to your career.
- This course is for those aspirants who aim to learn convolutional networks in particular and concepts related to them.
Computer Vision Executive Education Program
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- via Carnegie Mellon University
- 100 Hours of effort required!
- Study Type: Self-paced
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Content | Engagement | Practice | Career Benefit |
Good ★★★★☆ |
Fair ★★★☆☆ |
Fair ★★★☆☆ |
Fair ★★★☆☆ |
In this extensive training comprised of 10 perfectly designed modules, you will learn about Core image processing methods and understand the multiple techniques incorporated in them. Also, Image detection and objection recognition by using neural networks, Fundamental knowledge about geometrical visions and deriving useful 3D information out of those images, Useful techniques used in the alignment of objects in a video, and much more.
- The sole reason for choosing this course is its practical applicability and the rich content offered in it.
- This program is viable for those who have prior experience in Python and advanced calculus and linear algebra (ACLA). Also, this course will prove to be useful for those who are willing to give their career a thrust by acquiring a certification from a renowned school.
Become a Computer Vision Expert
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- via Udacity
- 180 Hours of effort required!
- Study Type: Self-paced
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Content | Engagement | Practice | Career Benefit |
Excellent ★★★★★ |
Good ★★★★☆ |
Good ★★★★☆ |
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This course offers leading-edge computer vision and DL techniques. This course will start with fundamental image processing and take you to the complexities of developing and customizing convolutional neural networks.
- The reason why we chose this course is that this course gives a career-boosting skill that makes your resume more professional. Concepts related to object tracking and facial recognition are a few of the many key take-away of the course.
- This course is an advanced version, so this course is best suited for those individuals who possess Prior knowledge of Python from the intermediate level to a bit above, have moderate information regarding statistics, especially probability, have Intermediate knowledge of ML techniques, and have been involved in working basic neural networks.
continue with more Computer Vision Courses & Certification Programs…
Modern Computer Vision™ PyTorch, Tensorflow2 Keras & OpenCV4
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- Rajeev D. Ratan via Udemy
- 9,091+ already enrolled!
- ★★★★★ (980 Ratings)
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Online Course Effectiveness Score | |||
Content | Engagement | Practice | Career Benefit |
Good ★★★★☆ |
Good ★★★★☆ |
Good ★★★★☆ |
Fair ★★★☆☆ |
This course offers foundational knowledge related to computer vision using OpenCV followed by concepts of deep learning (DL). Also, this course includes concepts like Graphics control and operation, Facial recognition and object detection,2D and 3D image illustrations, and much more
- The reason that brought about the selection of the course is the engaging nature of the course.
- This course covers a wide spectrum of participants from software developers to high school students looking to get a kick start in computer vision.
Computer Vision for Engineering and Science Specialization
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- MathWorks via Coursera
- 36 Hours of effort required!
- ★★★★★ (08 Ratings)
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Content | Engagement | Practice | Career Benefit |
Good ★★★★☆ |
Good ★★★★☆ |
Good ★★★★☆ |
Fair ★★★☆☆ |
This course offers top-notch skills that are in demand right now. In this course, you will carry out different projects involving object detection, acquire skillful training in models of image classification, learn methods for image alignment and tracking of objects, and much more.
- The reason why we chose this course is that it offers advanced knowledge on specialized topics related to computer vision.
- This course is for those individuals who aspire to acquire expertise in computer vision but should have prior experience related to image processing.
Python for Computer Vision with OpenCV and Deep Learning
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- Jose Portilla via Udemy
- 50,983+ already enrolled!
- ★★★★★ (9,343 Ratings)
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Content | Engagement | Practice | Career Benefit |
Good ★★★★☆ |
Good ★★★★☆ |
Fair ★★★☆☆ |
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In this course, you will learn how to integrate Python with Computer vision to process Audio/visual data. NumPy library is the most appropriate to use for numerical processing, so this course will teach you how to extract and manipulate image data. Participants will also get the opportunity to learn the OpenCV library for image opening and basics. Modern DL topics that include custom image classification and image recognition followed by YOLO are also included in this course.
- This course is selected because it provides great skills which are in demand in the market.
- Python beginners may find difficulty in coping with the course. This course is well-suited for Python developers.
Python Project: pillow, tesseract, and opencv
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- University of Michigan via Coursera
- 62,693+ already enrolled!
- ★★★★☆ (1,810 Ratings)
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Online Course Effectiveness Score | |||
Content | Engagement | Practice | Career Benefit |
Excellent ★★★★★ |
Good ★★★★☆ |
Good ★★★★☆ |
Fair ★★★☆☆ |
In this course you will learn the use of Pillow (Pyhton image library) for image manipulations, the use of Tesseract and Py-tesseract for optical detection and recognition of text, the use of OpenCV for facial and object detection plus recognition, developing Data analysis project of the data obtained from live sources using the aforementioned libraries.
- The reason why we chose this course is that it offers rich content which has greater applicability.
- This course is the best compliment for those participants who are already aware of Python programming skills and aspiring to gain hands-on experience and improve practical knowledge.
Deep Learning: Advanced Computer Vision (GANs, SSD, +More!)
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- Lazy Programmer Inc. via Udemy
- 34,146+ already enrolled!
- ★★★★★ (5,401 Ratings)
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Online Course Effectiveness Score | |||
Content | Engagement | Practice | Career Benefit |
Good ★★★★☆ |
Good ★★★★☆ |
Good ★★★★☆ |
Fair ★★★☆☆ |
In this course you will learn the Conversion of a CNN into an object detection system, the Use of SSD algorithm having more accuracy than the previous one, the Understanding of neural style transfer, the Combining content image and style image, the Use of GAN for the development, Use of object localization for the detection of objects.
- The reason which brought about the selection of this course is that it offers perfect integration of basic CNN architecture with advanced and new architectures such as VGG and Inception. This has high market demand.
- This course is a perfect fit even for beginners but must who have prior basic knowledge of mathematical concepts.
Advanced Computer Vision with TensorFlow
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- DeepLearning.AI via Coursera
- 25,994+ already enrolled!
- ★★★★★ (409 Ratings)
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Online Course Effectiveness Score | |||
Content | Engagement | Practice | Career Benefit |
Good ★★★★☆ |
Good ★★★★☆ |
Good ★★★★☆ |
Fair ★★★☆☆ |
In this course you will learn about object detection and recognition, building your models for the detection, localization, and manipulation of the image, the Use of FCN and its variations and complexities, and Advanced ML techniques including saliency maps and class activation maps
- The reason why we chose this course is that it helps the participants to develop skills that have high practical usage in the workplace.
- This course is for beginners as well as intermediate-level ML professionals looking to improve their knowledge of TensorFlow.
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