关于此 专项课程

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Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills. The Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker. This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages who want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud. Each of the 10 weeks features a comprehensive lab developed specifically for this Specialization that provides hands-on experience with state-of-the-art algorithms for natural language processing (NLP) and natural language understanding (NLU), including BERT and FastText using Amazon SageMaker.
可分享的证书
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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。
高级
完成课程大约需要 3 个月
建议进度:4 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。
高级
完成课程大约需要 3 个月
建议进度:4 小时/周
英语(English)

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此专项课程包含 3 门课程

课程1

课程 1

Analyze Datasets and Train ML Models using AutoML

4.6
126 个评分
27 条评论
课程2

课程 2

Build, Train, and Deploy ML Pipelines using BERT

4.7
56 个评分
9 条评论
课程3

课程 3

Optimize ML Models and Deploy Human-in-the-Loop Pipelines

4.8
32 个评分
9 条评论

提供方

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deeplearning.ai

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亚马逊网络服务系统

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