Computer Vision is one of the most exciting fields in Machine Learning and AI. It has applications in many industries, such as self-driving cars, robotics, augmented reality, and much more. In this beginner-friendly course, you will understand computer vision and learn about its various applications across many industries.
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IBM Skills NetworkОб этом курсе
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Сертификат, ссылками на который можно делиться с другими людьми
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100% онлайн
Начните сейчас и учитесь по собственному графику.
Начальный уровень
Прибл. 22 часа на выполнение
Английский
Субтитры: Английский
Приобретаемые навыки
- Deep Learning
- Opencv
- Artificial Intelligence (AI)
- Image Processing
- Computer Vision
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Начальный уровень
Прибл. 22 часа на выполнение
Английский
Субтитры: Английский
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IBM Skills Network
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
Программа курса: что вы изучите
1 ч. на завершение
Introduction to Computer Vision
In this module, we will discuss the rapidly developing field of image processing. In addition to being the first step in Computer Vision, it has broad applications ranging anywhere from making your smartphone's image look crystal clear to helping doctors cure diseases.
1 ч. на завершение
4 видео ((всего 16 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
6 ч. на завершение
Image Processing with OpenCV and Pillow
Image processing enhances images or extracts useful information from the image. In this module, we will learn the basics of image processing with Python libraries OpenCV and Pillow.
6 ч. на завершение
6 видео ((всего 41 мин.))
5 ч. на завершение
Machine Learning Image Classification
In this module, you will Learn About the different Machine learning classification Methods commonly used for Computer vision, including k nearest neighbours, Logistic regression, SoftMax Regression and Support Vector Machines. Finally, you will learn about Image features.
5 ч. на завершение
8 видео ((всего 37 мин.))
4 ч. на завершение
Neural Networks and Deep Learning for Image Classification
In this module, you will learn about Neural Networks, fully connected Neural Networks, and Convolutional Neural Network (CNN). You will learn about different components such as Layers and different types of activation functions such as ReLU. You also get to know the different CNN Architecture such as ResNet and LenNet.
4 ч. на завершение
4 видео ((всего 20 мин.))
Рецензии
- 5 stars65,04 %
- 4 stars20,75 %
- 3 stars6,67 %
- 2 stars3,27 %
- 1 star4,24 %
Лучшие отзывы о курсе INTRODUCTION TO COMPUTER VISION AND IMAGE PROCESSING
от партнера SM12 дек. 2020 г.
Course is good but Watson service in IBM Cloud ran into issues repeatedly, Unfortunately! I hope IBM and community will be able to support and guide better. Thanks for the course.
от партнера SB1 июля 2020 г.
Well Explained about each and every tools and how to train and test the models and deploy into the cloud services I had very good expreience in collabrating into IBM
от партнера KK16 апр. 2020 г.
This is one of the best course by IBM. I specifically enjoyed Computer Vision modelling and its related project and also enjoyed the way team put in effort for designing this course.
от партнера GG18 июля 2022 г.
Very interesting course to go for if you are interested in furthering your understanding in computer vision concepts
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