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 |
Online Course Effectiveness Score | |||
Content | Engagement | Practice | Career Benefit |
Good ★★★★☆ | Good ★★★★☆ | 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.
Online Course Effectiveness Score | |||
Content | Engagement | Practice | Career Benefit |
Excellent ★★★★★ | Fair ★★★☆☆ | Good ★★★★☆ | Good ★★★★☆ |
This course not only provides knowledge regarding computer vision and how to use it but also allows maximizing its utilization.
Online Course Effectiveness Score | |||
Content | Engagement | Practice | Career Benefit |
Excellent ★★★★★ | Good ★★★★☆ | Good ★★★★☆ | Fair ★★★☆☆ |
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.
Online Course Effectiveness Score | |||
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.
Online Course Effectiveness Score | |||
Content | Engagement | Practice | Career Benefit |
Excellent ★★★★★ | Good ★★★★☆ | Good ★★★★☆ | Good ★★★★☆ |
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.
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
Online Course Effectiveness Score | |||
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.
Online Course Effectiveness Score | |||
Content | Engagement | Practice | Career Benefit |
Good ★★★★☆ | Good ★★★★☆ | Fair ★★★☆☆ | Fair ★★★☆☆ |
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.
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.
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.
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
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