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.
oferecido por
IBMInformações sobre o curso
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Prazos flexíveis
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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível iniciante
Aprox. 22 horas para completar
Inglês
Legendas: Inglês
Habilidades que você terá
- Deep Learning
- Opencv
- Artificial Intelligence (AI)
- Image Processing
- Computer Vision
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível iniciante
Aprox. 22 horas para completar
Inglês
Legendas: Inglês
oferecido por
IBM
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.
Programa - O que você aprenderá com este curso
1 hora para concluir
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 hora para concluir
4 vídeos (Total 16 mín.), 2 leituras, 2 testes
6 horas para concluir
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 horas para concluir
6 vídeos (Total 41 mín.)
5 horas para concluir
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 horas para concluir
8 vídeos (Total 37 mín.)
4 horas para concluir
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 horas para concluir
4 vídeos (Total 20 mín.)
Avaliações
Principais avaliações do INTRODUCTION TO COMPUTER VISION AND IMAGE PROCESSING
por MO12 de Nov de 2019
Thoroughly enjoyed this course. Learned about OpenCV a bit and added to my small knowledge of Python. The ability to know how to train Watson to do optical recognizition will be invaluable.
por KK16 de Abr de 2020
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.
por SS19 de Ago de 2019
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.
por SM12 de Dez de 2020
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.
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