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Arpan Konar

成为会员时间：2023

白银联赛

13400 积分
Google 表格中的 Gemini Earned Jan 5, 2024 EST
Google 文档中的 Gemini Earned Jan 5, 2024 EST
Gmail 中的 Gemini Earned Jan 5, 2024 EST
内置 Gemini 的 Google Workspace：AI 工作流基础知识 Earned Jan 5, 2024 EST
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud Earned Jan 4, 2024 EST
Analyzing and Visualizing Data in Looker Earned Jan 4, 2024 EST
在 Google Cloud 上为机器学习 API 准备数据 Earned Jan 2, 2024 EST
Machine Learning Operations (MLOps): Getting Started Earned Dec 31, 2023 EST
Launching into Machine Learning Earned Dec 31, 2023 EST
Google Cloud 上的 AI 和机器学习简介 Earned Dec 28, 2023 EST

本课程介绍如何使用 Google 表格中的 Gemini 自动整理数据和处理复杂数字。了解如何根据原始文本构建表格、使用自然语言自动填充数据、按需生成原生电子表格公式，以及快速提取高级分析洞见或可视化图表，而无需具备高级电子表格专业知识。

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本课程介绍如何使用 Google 文档中的 Gemini 来简化写作和内容创作。了解如何从头开始起草文档，使用互动式工具优化写作语气和清晰度，总结复杂文件，以及将长篇文本无缝转换为适用于其他 Google Workspace 应用的结构化资产。

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本课程介绍如何使用 Gmail 中的 Gemini 来优化邮件管理。了解如何总结冗长的会话串、使用自然语言检索信息、起草专业回复，以及直接在收件箱中整理沟通策略。

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Gemini for Google Workspace 可在 Google Workspace 中为客户提供生成式 AI 功能。在本学习路线中，您将了解 Gemini 的主要功能，以及如何在 Google Workspace 中使用它们来提高工作效率。

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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.

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完成入门级技能徽章课程在 Google Cloud 上为机器学习 API 准备数据，展示以下技能： 使用 Dataprep by Trifacta 清理数据、在 Dataflow 中运行数据流水线、在 Managed Service for Apache Spark 中创建集群和运行 Apache Spark 作业，以及调用机器学习 API，包括 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。

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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.

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本课程介绍 Google Cloud 的 AI 和机器学习 (ML) 能力，重点讲解如何开发生成式和预测式 AI 项目。本课程将探讨“数据到 AI”全生命周期中的多种技术、产品和工具，并通过互动练习帮助数据科学家、AI 开发者和机器学习工程师提升专业能力。

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