Computer Vision Fundamentals with Google Cloud
This course provides an overview of computer vision fundamentals and explores how to use Google Cloud to implement machine learning strategies for various computer vision use cases. Learn how to apply the latest technologies to solve real-world problems. ▼
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Course Feature
Cost:
Free Trial
Provider:
Pluralsight
Certificate:
Paid Certification
Language:
English
Start Date:
On-Demand
Course Overview
❗The content presented here is sourced directly from Pluralsight platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [February 21st, 2023]
Learners can learn the fundamentals of computer vision and how to apply them to real-world problems. They will gain an understanding of the different types of computer vision use cases and the machine learning strategies used to solve them. They will learn how to build and optimize their own image classification models using linear models, deep neural networks, and convolutional neural networks. They will also learn how to improve model accuracy with augmentation, feature extraction, and hyper-parameter tuning. Finally, they will gain an understanding of practical issues that arise when working with computer vision, such as how to incorporate the latest research findings and how to work with limited data.
Course Provider
Provider Pluralsight's Stats at AZClass
Pluralsight ranked 16th on the Best Medium Workplaces List.
Pluralsight ranked 20th on the Forbes Cloud 100 list of the top 100 private cloud companies in the world.
Pluralsight Ranked on the Best Workplaces for Women List for the second consecutive year.
AZ Class hope that this free trial Pluralsight course can help your Computer Vision skills no matter in career or in further education. Even if you are only slightly interested, you can take Computer Vision Fundamentals with Google Cloud course with confidence!
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Quiz
Submitted Sucessfully
1. What type of models can be used to build custom image classifiers?
2. What is the purpose of augmentation?
3. What is the purpose of feature extraction?
4. What is the purpose of fine-tuning hyper-parameters?
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