Google Cloud
Managing Machine Learning Projects with Google Cloud
Google Cloud

Managing Machine Learning Projects with Google Cloud

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包含在 Coursera Plus

深入了解一个主题并学习基础知识。
4.6

(3,725 条评论)

初级 等级
无需具备相关经验
灵活的计划
1 周 在 10 小时 一周
自行安排学习进度
92%
大多数学生喜欢此课程
深入了解一个主题并学习基础知识。
4.6

(3,725 条评论)

初级 等级
无需具备相关经验
灵活的计划
1 周 在 10 小时 一周
自行安排学习进度
92%
大多数学生喜欢此课程

您将学到什么

  • Explore common machine learning use cases implemented by businesses.

  • Assess the feasibility of your own ML use case and its ability to meaningfully impact your business.

  • Identify the requirements to build, train, and evaluate an ML model.

  • Define data characteristics and biases that affect the quality of ML models and recognize key considerations for managing ML projects.

要了解的详细信息

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作业

6 项作业

授课语言:英语(English)

了解顶级公司的员工如何掌握热门技能

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Digital Transformation Using AI/ML with Google Cloud 专项课程 专项课程的一部分
在注册此课程时,您还会同时注册此专项课程。
  • 向行业专家学习新概念
  • 获得对主题或工具的基础理解
  • 通过实践项目培养工作相关技能
  • 获得可共享的职业证书

该课程共有8个模块

Welcome to the course! In this module, you'll meet the instructor and learn about the course content and how to get started.

涵盖的内容

1个视频2篇阅读材料

This module begins by defining machine learning at a high level and then helps you gain a thorough understanding of its value for business by reviewing several real-world examples. It then introduces machine learning projects and provides practice using a tool to assess the feasibility of several ML problems.

涵盖的内容

4个视频1篇阅读材料1个作业

This module begins by defining machine learning at a high level and then helps you gain a thorough understanding of its value for business by reviewing several real-world examples. It then introduces machine learning projects and provides practice using a tool to assess the feasibility of several ML problems.

涵盖的内容

9个视频1篇阅读材料1个作业

After you have assessed the feasibility of your supervised ML problem, you're ready to move to the next phase of an ML project. This module explores the various considerations and requirements for building a complete dataset in preparation for training, evaluating, and deploying an ML model. It also includes two demos—Vision API and AutoML Vision—as relevant tools that you can easily access yourself or in partnership with a data scientist. You'll also have the opportunity to try out AutoML Vision with the first hands-on lab.

涵盖的内容

6个视频1个作业1个应用程序项目

Data in the world is inherently biased, and that bias can be amplified through ML solutions. In this module, you'll learn about some of the most common biases and how they can disproportionately affect or harm an individual or groups of individuals. You'll also be given guidelines for uncovering possible biases at each phase of an ML project and strategies for achieving ML fairness as much as possible.

涵盖的内容

6个视频1个作业1个应用程序项目

This module explores 5 general themes for discovering ML use cases within day-to-day business, followed by concrete customer examples. You'll learn about creative applications of ML, such as improving the resolution of images or generating music.

涵盖的内容

6个视频1篇阅读材料1个作业1个应用程序项目

When you thoroughly understand the fundamentals of machine learning and considerations within in each phase of the project, you're ready to learn about the best practices for managing an ML project. This module describes 5 key considerations for successfully managing an ML project end-to-end: identifying the business value, developing a data strategy, establishing data governance, building successful ML teams, and enabling a culture of innovation. You'll also have an opportunity to gain further exposure to one of Google Cloud's tools by completing a final hands-on lab: Evaluate an ML Model with BigQuery ML.

涵盖的内容

7个视频1个作业1个应用程序项目

This module provides a summary of the key points covered in each of the modules in the course.

涵盖的内容

1个视频

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