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.
About this Course
Skills you will gain
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
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- 4 stars22.86%
- 3 stars6.39%
- 2 stars1.74%
- 1 star0.77%
TOP REVIEWS FROM COMPUTER VISION FUNDAMENTALS WITH GOOGLE CLOUD
The course provides an excellent overview of Image Understanding with TF and the utilization of all the capabilities of GCP to build productionable image systems.
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 !
Fairly basic course and would have liked more guidance on setting up jobs for processing including sample JSON requests etc. However was interesting but definitely not challenging.
Great TPU Exploration.
Mr LEK Is very cool and his explanation about the topic is sound easy.
About the Advanced Machine Learning on Google Cloud Specialization
This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization picks up where “Machine Learning on GCP” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a course on building recommendation systems. Topics introduced in earlier courses are referenced in later courses, so it is recommended that you take the courses in exactly this order.
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