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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/gcp-big-data-ml-fundamentals
课程评论：没有评论
课程名称：Google Cloud大数据与机器学习基础 概述：本课程介绍了支持数据到人工智能生命周期的Google Cloud大数据和机器学习产品与服务。课程探讨了构建大数据管道和使用Vertex AI构建机器学习模型的过程、挑战和好处。 课程大纲： 1. 课程介绍 - 描述：欢迎学习者参加大数据与机器学习基础课程，提供课程结构和目标的概述。 2. Google Cloud上的大数据与机器学习 - 描述：探讨Google Cloud基础设施的关键组成部分，介绍支持数据到人工智能生命周期的多种大数据和机器学习产品与服务。 3. 流数据的数据工程 - 描述：介绍Google Cloud管理流数据的解决方案，考察一个端到端管道，包括使用Pub/Sub进行数据摄取，使用Dataflow进行数据处理，以及使用Looker和Data Studio进行数据可视化。 4. 使用BigQuery的大数据 - 描述：向学习者介绍BigQuery，Google的完全托管、无服务器数据仓库，同时探讨BigQuery ML以及用于构建自定义机器学习模型的流程和关键命令。 5. Google Cloud上的机器学习选项 - 描述：探讨在Google Cloud上构建机器学习模型的四种不同选项，同时介绍Vertex AI，这一Google的统一平台，用于构建和管理机器学习项目的生命周期。 6. Vertex AI的机器学习工作流程 - 描述：集中于Vertex AI中机器学习工作流程的三个关键阶段——数据准备、模型训练和模型准备。学习者将有机会实践使用AutoML构建机器学习模型。 7. 课程总结 - 描述：回顾课程中涵盖的主题，并提供进一步学习的额外资源。 此课程为希望了解大数据和机器学习基础知识以及如何在Google Cloud中应用这些知识的学习者提供了全面的学习体验。
Name:Course Introduction
Description:This section welcomes learners to the Big Data and Machine Learning Fundamentals course, and provides an overview of the course structure and goals.
Name:Big Data and Machine Learning on Google Cloud
Description:This section explores the key components of Google Cloud's infrastructure. It's here that we introduce many of the big data and machine learning products and services that support the data-to AI lifecycle on Google Cloud.
Name:Data Engineering for Streaming Data
Description:This section introduces Google Cloud's solution to managing streaming data. It examines an end-to-end pipeline, including data ingestion with Pub/Sub, data processing with Dataflow, and data visualization with Looker and Data Studio.
Name:Big Data with BigQuery
Description:This section introduces learners to BigQuery, Google's fully-managed, serverless data warehouse. It also explores BigQuery ML, and the processes and key commands that are used to build custom machine learning models.
Name:Machine Learning Options on Google Cloud
Description:This section explores four different options to build machine learning models on Google Cloud. It also introduces Vertex AI, Google's unified platform for building and managing the lifecycle of ML projects.
Name:The Machine Learning Workflow with Vertex AI
Description:This section focuses on the three key phases--data preparation, model training, and model preparation--of the machine learning workflow in Vertex AI. Learners get the opportunity to practice building a machine learning model with AutoML.
Name:Course Summary
Description:This section reviews the topics covered in the course, and provides additional resources for further learning.
This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
