关于此 专项课程

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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, 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. > By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <
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中级
完成课程大约需要 5 个月
建议进度:5 小时/周
英语(English)
可分享的证书
完成后获得证书
100% 在线课程
立即开始,按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
中级
完成课程大约需要 5 个月
建议进度:5 小时/周
英语(English)

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此专项课程包含 5 门课程

课程1

课程 1

How Google does Machine Learning

4.6
6,699 个评分
1,055 条评论
课程2

课程 2

Launching into Machine Learning

4.6
4,074 个评分
465 条评论
课程3

课程 3

TensorFlow 简介

4.4
2,602 个评分
322 条评论
课程4

课程 4

特色工程

4.5
1,656 个评分
181 条评论

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Google 云端平台

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