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What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow and scale-out those models’ training. and offer high-performance predictions. Convert raw data to features to allow ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform.
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Applied Learning Project
This specialization incorporates hands-on labs using our Qwiklabs platform.
These hands-on components will let you apply the skills you learn in the video lectures. Projects will incorporate topics such as Google Cloud Platform products, which are used and configured within Qwiklabs. You can expect to gain practical hands-on experience with the concepts explained throughout the modules.
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 the
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 and 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.