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Débute 4 June 2026 07:25

Se termine 4 June 2026

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Introduction to Computer Vision and Image Processing

Embarquez dans un voyage captivant à travers le monde de la Vision par Ordinateur et le Traitement d'Images avec ce cours accessible aux débutants, proposé sur Coursera, destiné aux personnes désireuses de plonger dans les domaines de l'Apprentissage Automatique et de l'IA. Découvrez le pouvoir transformateur de la Vision par Ordinateur, une techno.
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Embark on a journey into the captivating world of Computer Vision and Image Processing with this beginner-friendly course offered on Coursera, targeted at individuals eager to delve into the realms of Machine Learning and AI. Discover the transformative power of Computer Vision, a pivotal technology behind innovations like self-driving cars, robotics, augmented reality, and countless other applications across diverse sectors.

Without necessitating prior expertise in Machine Learning or Computer Vision, this course, however, recommends a foundational understanding of Python and basic high school mathematics.

Engage in hands-on learning through Python, leveraging essential libraries such as Pillow and OpenCV to master basic image processing techniques. Progress to more complex tasks including image classification and object detection, guided by practical exercises and labs.

Labs are designed to offer a comprehensive learning experience, combining the use of Jupyter Labs and Computer Vision Learning Studio (CV Studio) - a free, innovative tool that facilitates the upload, training, and testing of custom image classifiers and detection models.

By the culmination of this course, participants will have the skillset to craft their own computer vision web application and deploy it to the cloud, marking a significant milestone in their learning journey.

Categories include Python Courses, Computer Vision Courses, and Digital Image Processing Courses, making this an indispensable learning path for aspiring professionals in these fields, brought to you by Coursera.


Enseigné par

Yi Leng Yao and Sacchit Chadha


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