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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/google-machine-learning
课程评论：没有评论
课程名称：谷歌如何进行机器学习 概述：本课程旨在探讨在谷歌云上实施机器学习的最佳实践，介绍了 Vertex AI 平台及其如何帮助用户在不编写任何代码的情况下快速构建、训练和部署 AutoML 机器学习模型。同时，课程解释了什么是机器学习以及其可以解决的各种问题。谷歌对机器学习的看法独特，它关注于提供一个统一的平台，管理数据集、特征存储，以及构建、训练和部署机器学习模型的能力。此外，课程还讨论了将候选用例转化为机器学习驱动的五个阶段，并强调遵循这些阶段的重要性，最后提到机器学习可能放大偏见的现象及其识别方法。 课程大纲： 1. 课程及系列介绍：本模块介绍了本课程系列及授课的谷歌专家。 2. 成为 AI 优先的含义：探讨围绕机器学习建立数据战略的内容。 3. 谷歌的机器学习方法：分享谷歌多年来积累的组织知识。 4. 使用 Vertex AI 进行机器学习开发：讨论机器学习如何从目标开始，并回顾模型是否准备好进入生产 “概念验证” 或 “实验” 阶段的过程。 5. 使用 Vertex Notebooks 进行机器学习开发：探讨在 Vertex AI 中的托管笔记本和用户托管笔记本。 6. 在 Vertex AI 上实施机器学习的最佳实践：回顾在 Vertex AI 中多个机器学习过程的最佳实践。 7. 负责任的 AI 开发：讨论机器学习系统为何默认不公正，以及在将 ML 融入产品时需要考虑的因素。 8. 课程总结：总结谷歌如何进行机器学习课程的主要内容。 该课程适合对机器学习和谷歌云平台有兴趣的学习者，旨在提供实用的知识与技能。
Name:Introduction to Course and Series
Description:This module introduces the course series and the Google experts who will be teaching it.
Name:What It Means to be AI-First
Description:In this module, you explore building a data strategy around machine learning.
Name:How Google Does ML
Description:This module shares the organizational know-how Google has acquired over the years.
Name:Machine Learning Development with Vertex AI
Description:All machine learning starts with some type of goal - whether it be a business use case, academic use case, or goal you are trying to solve. This module reviews the process of determining whether the model is ready for production the “proof of concept” or “experimentation” phase.
Name:Machine Learning Development with Vertex Notebooks
Description:This module explores both managed notebooks and user-managed notebooks for machine learning development in Vertex AI.
Name:Best Practices for Implementing Machine Learning on Vertex AI
Description:This module reviews best practices for a number of different machine learning processes in Vertex AI.
Name:Responsible AI Development
Description:This module discusses why machine learning systems aren’t fair by default and some of the things you have to keep in mind as you infuse ML into your products.
Name:Summary
Description:This module is a summary of the How Google Does Machine Learning course.
What are best practices for implementing machine learning on Google Cloud? What is Vertex AI and how can you use the platform to quickly build, train, and deploy AutoML machine learning models without writing a single line of code? What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently: it’s about providing a unified platform for managed datasets, a feature store, a way to build, train, and deploy machine learning models without writing a single line of code, providing the ability to label data, create Workbench notebooks using frameworks such as TensorFlow, SciKit Learn, Pytorch, R, and others. Our Vertex AI Platform also includes the ability to train custom models, build component pipelines, and perform both online and batch predictions. We also discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important to not skip the phases. We end with a recognition of the biases that machine learning can amplify and how to recognize them.
