What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models.
Об этом курсе
Карьерные результаты учащихся
Карьерные результаты учащихся
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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Лучшие отзывы о курсе HOW GOOGLE DOES MACHINE LEARNING
Great to know how to do machine learning in scale and to know the common pitfalls people may fall into while doing ML. Provides great hands-on training on GCP and get to know various API's GCP offers.
Really easy with all instruction.I didnt feel bored at any point gave me the basic idea of what is machine learning and how easy google made API's and cloud platform for machine learning Thank you
Nice course to get new insights about ML applicability and to get familiar with many tools of GCP. Encourages to think about ML in more general way then just collecting the data and training models.
It is very informative about the machine learning and AI usage in Google products and provide deep dive into GCP platform in very intuitive way. I recommend to AI aficionado. Thanks you Google!!!
Специализация Machine Learning with TensorFlow on Google Cloud Platform: общие сведения
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, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows 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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