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***NEW! Specialization Completion Challenge, receive Qwiklabs credits valued up to $150! See below for details.***
We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using the Cloud Machine Learning Engine. Course Objectives: Create machine learning models in TensorFlow Use the TensorFlow libraries to solve numerical problems Troubleshoot and debug common TensorFlow code pitfalls Use tf.estimator to create, train, and evaluate an ML model Train, deploy, and
SPECIALIZATION COMPLETION CHALLENGE
As if learning new skills wasn’t enough of an incentive, we’re excited to announce a special completion challenge for ‘Machine Learning with TensorFlow on Google Cloud Platform’ specialization. Here’s how it works: Our completion challenge runs through 11:59 pm PT May 5, 2019. Complete any course in this Specialization including this one, anytime in this period and we’ll send you 30 Qwiklabs credits for each course completed (up to $150 value given there are 5 courses in the specialization). You can use these credits to take additional labs and earn badges, which you can then add to your resume and social profiles. Your challenge awaits – begin learning on Coursera today!
What will students learn?
Learn best practices for using TensorFlow, a popular open-source machine learning framework
Build a basic neural network in TensorFlow
Train a neural network for a computer vision application
Understand how to use convolutions to improve your neural network
Skills Gained in this Course:
Key Information About This Course:
100% online: Start instantly and learn at
Flexible schedule and deadlines: Set and maintain flexible deadlines
Duration: Approx. 6 hours to complete, suggested 4 weeks of study, 4-5 hours/week.
Subtitles: English only
What is it?
The Coursera Global Skills Index benchmarks 60 countries and 10 industries across Business, Technology, and Data Science. It also reveals trending skills in those regions and industries.
Why did we create it?
The skills landscape is in transition and companies, countries, and individuals are grappling with a serious skills shortage, in addition to uncertainty on which skills they need to invest in to succeed in the changing economy. We hope that this report, and future reports, can help them create tailored talent strategies that enable a more competitive future—and let them know that Coursera can help them get there.