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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning-business-professionals
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课程名称：使用Google Cloud管理机器学习项目 课程概述：本课程旨在为非技术角色的商业专业人士提供有关机器学习项目的领导或影响能力。如果您对机器学习有疑问并希望了解如何使用它，而不必了解复杂的技术术语，那么本课程将十分适合您。您将学习如何将商业问题转化为机器学习的使用案例，并评估其可行性和影响力。此外，您将探索意想不到的使用情况，识别机器学习项目的不同阶段以及每个阶段的考虑因素，从而增强您向团队或领导提议定制机器学习使用案例的信心，以及将需求转达给技术团队的能力。 课程大纲： 模块1：介绍 欢迎来到课程！在本模块中，您将认识讲师，并了解课程内容和如何开始学习。 模块2：识别机器学习的商业价值 本模块以高层次的定义机器学习为开端，随后通过几个真实案例深入了解其对商业的价值。课程还介绍了机器学习项目，并提供实践使用工具评估几个机器学习问题的可行性。 模块3：定义机器学习作为实践 本模块继续高层次定义机器学习，并帮助您了解其商业价值，通过回顾多个实际案例进行深入分析。此外，本模块还介绍机器学习项目，并提供实践使用工具评估多个机器学习问题的可行性。 模块4：构建和评估机器学习模型 评估了监督学习问题的可行性后，您将进入机器学习项目的下一阶段。本模块探讨构建完整数据集进行训练、评估和部署机器学习模型所需的各种考虑因素和要求。包括两个演示——视觉API和AutoML Vision，您可以轻松访问这些工具，或与数据科学家合作使用。您还有机会在第一次实践实验室中尝试AutoML Vision。 模块5：负责任与伦理地使用机器学习 世界中的数据本质上是有偏见的，这种偏见可以通过机器学习解决方案被放大。本模块将让您了解一些最常见的偏见及其如何对个人或团体产生不成比例的影响或伤害。您还将获得在机器学习项目每个阶段发现潜在偏见的指南，以及尽可能实现机器学习公平性的策略。 模块6：在日常业务中发现机器学习使用案例 本模块探讨了五个通用主题，以发掘日常业务中的机器学习使用案例，并提供具体的客户示例。您将了解机器学习的创意应用，例如图像分辨率的提升或音乐生成。 模块7：成功管理机器学习项目 当您充分理解机器学习的基础和项目每个阶段的考虑因素后，您将学习管理机器学习项目的最佳实践。本模块描述了成功管理机器学习项目的五个关键考虑因素：识别商业价值、制定数据战略、建立数据治理、构建成功的机器学习团队，以及促进创新文化。同时，您将有机会通过完成最终的实践实验室——用BigQuery ML评估机器学习模型，进一步接触Google Cloud的工具。 模块8：总结 本模块总结了课程中每个模块涵盖的关键点。
Name:Module 1: Introduction
Description:Welcome to the course! In this module, you'll meet the instructor and learn about the course content and how to get started.
Name:Module 2: Identifying business value for using ML
Description: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.
Name:Module 3: Defining ML as a practice
Description: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.
Name:Module 4: Building and evaluating ML models
Description: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.
Name:Module 5: Using ML responsibly and ethically
Description: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.
Name:Module 6: Discovering ML use cases in day-to-day business
Description: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.
Name:Module 7: Managing ML projects successfully
Description: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.
Name:Module 8: Summary
Description:This module provides a summary of the key points covered in each of the modules in the course.
Business professionals in non-technical roles have a unique opportunity to lead or influence machine learning projects. If you have questions about machine learning and want to understand how to use it, without the technical jargon, this course is for you. Learn how to translate business problems into machine learning use cases and vet them for feasibility and impact. Find out how you can discover unexpected use cases, recognize the phases of an ML project and considerations within each, and gain confidence to propose a custom ML use case to your team or leadership or translate the requirements to a technical team.
