This post will provide you the most relevant Computer Vision Tutorial in 2019 that is available for free to enroll and learn. Human vision is amazingly beautiful and complex. It all started billions of years ago when small organisms developed a mutation that made them sensitive to light. Fast forward to today, there is an abundance of life on the planet which all have very similar visual systems. They include eyes for capturing light, receptors in the brain for accessing it, and visual cortex for processing it. Genetically engineered, and balanced pieces of a system that help us do things as simple as appreciating a sunrise.
But this is just the beginning. In the past 30 years, we’ve made even more strides to extending this visual is visual ability, not just to ourselves, but to the machine as well. Computer vision is taking on increasingly complex challenges and is seeing accuracy that rivals humans performing the same image recognition tasks. Many of the professionals are looking to pursue knowledge and learning in this field.
Here are some amazing Computer Vision Tutorials for free
1. Python Projects: OpenCV, Pillow, Tesseract by University of Michigan
This course is available at Coursera. This course is suitable for someone who has already learned python, now wants to clear him/ her hand on real-world data analysis for their benefits. This is programmed for helping the learner to ace in the area of computer vision.
An intro to third party API’s, manipulation of images using python imaging library, applying optical character recognition to the images to recognize text, identifying faces image using the popular OpenCV library. It has flexible deadlines. The course is an intermediate level provided by English subtitles. This certification will help you by going through a hands-on project suitable for a portfolio.
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This is a 7 hours course available on Udemy at aa very affordable pricing. You’ll learn how to create an object detection system, locating objects predicting the labels and classification of images. You’ll learn the use of state-of-the-art convolutional neural nets such as VGG. All the topics are explained with proper examples and demonstrations. Downloading all the code used from the Github of the instructor could also be done easily.
This certification is again available at Coursera. Its duration goes for 2 Months with an average of 14 hours per week. The specialization is done by using MATLAB using computer vision and supporting toolboxes. The learner should have some basic experience and programming skills, especially in MATLAB.
The learner will explore the integral elements that enable vision apps, ranging from editing images to reading traffic signs in self-driving cars to navigate around human co-workers. Content includes image processing and state-of-the-art vision technologies and augmented insights.
This is a Coursera course. It takes about 4 weeks to complete, 4 -5 hours per week. This course will help you in building the convolutional neural networks and how to apply it to image data. Thanks to deep learning, Computer vision is enabling numerous exciting applications ranging from safe autonomous driving to accurate face recognition.
By the end of this course, you’ll learn CNN including recent variation such as residual networks, applying a convolution network to visual detection and recognition tasks, using neural style transfer to generate art.
This course is available at Udemy. The course duration is of 11 hours. The course will help you to learn new things into the world of artificial intelligence and build amazing apps. This course has different methodologies, basic concepts, and process, applying them by practicing in real-world.
You get to learn about the theory behind the computer vision, mastering OpenCV, facial recognition, the toolbox for the powerful Computer vision models. You get better learning through examples that help you clear your topics.
I hope now you are aware of the best Computer Vision Tutorial in 2019. You must rush to these courses and enroll yourself to the journey of the future technology.