In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization (https://www.coursera.org/specializations/machine-learning-tensorflow-gcp).
About this Course
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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TOP REVIEWS FROM END-TO-END MACHINE LEARNING WITH TENSORFLOW ON GCP
Would be nicer if students can use the google cloud with less restrictions. I got blocked multiple times from trying the codes in the videos.. Overall, the materials are great and very interesting!
I would like to thank Lak and Chris for their wonderful presentation of the deployment of ML models on the Google Cloud Platform. The case study problem chosen for the course is also unique.
awesome learning experience fro the teacher from google. thanks to coursera and google for providing me such a good lesson which will be beneficial for my upcoming future and research work
Definitely adds a unique perspective on thinking about machine learning systems at scale. This course is suitable for Data Scientists, Data Engineers and Machine Learning Engineers.
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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