Computer Vision Fundamentals with Google Cloud

This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models. The course shows how to improve a model’s accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data.

The course also looks at practical issues that arise, for example, when one doesn’t have enough data and how to incorporate the latest research findings into different models.

Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.

• Understand at a high-level the types of problems computer vision may solve

• Understand some of the key concepts and model architectures typically used using computer vision

Syllabus

Syllabus - What you will learn from this course
Week 1
Introduction
Introduction to Computer Vision and Pre-built ML Models for Image Classification
Vertex AI and AutoML Vision on Vertex AI
Week 2
Custom Training with Linear, Neural Network and Deep Neural Network models
Convolutional Neural Networks
Dealing with Image Data
Summary

FAQ

Can I preview a course before enrolling?

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

What will I get when I enroll?

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

When will I receive my Course Certificate?

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

Why can’t I audit this course?

This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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Last Updated: September 1, 2015

Reviews

Good Practical Experience with the concepts what that I learned . Good for recommending my friends.

I would like to work with TPUs in one laboratory. Also, I would like to see how the pattern of image was formed throught the convolutional neural network in a lab.

An excellent course. Clear, concise and comprehensive.

a real eye opener education, it gave me lots of answers to the questions i had in this area. it is just amazing that ML can differ between roses and tulips !