Petr Klimeš
成为会员时间：2026
白银联赛
13139 积分
制作认证学习指南：AGWA 备考
Earned Nov 4, 2026 EST
AppSheet Administration
Earned Nov 4, 2026 EST
Google Cloud Database Migrations: Strategy
Earned Nov 4, 2026 EST
Build a Certification Study Guide: ACE Exam Prep
Earned Nov 4, 2026 EST
Build a Certification Study Guide: PCD Exam Prep
Earned Nov 4, 2026 EST
Build a Certification Study Guide: PDE Exam Prep
Earned Nov 4, 2026 EST
Build a Certification Study Guide: PMLE
Earned Nov 4, 2026 EST
Build a Certification Study Guide: PCDBE Exam Prep
Earned Nov 4, 2026 EST
利用 Gemini 实现 AI 赋能的数据库管理
Earned Nov 4, 2026 EST
Security Command Center: Identify and Prioritize Risks
Earned Nov 4, 2026 EST
Model Armor：保障 AI 部署安全
Earned Nov 4, 2026 EST
Security Command Center: Vulnerabilities and Misconfigurations
Earned Nov 3, 2026 EST
Google Threat Intelligence
Earned Nov 3, 2026 EST
适用于生成式 AI 的机器学习运维 (MLOps)
Earned Nov 3, 2026 EST
Introduction Google Security Operations (SOAR)
Earned Nov 3, 2026 EST
Introduction to Google Security Operations (SIEM)
Earned Nov 3, 2026 EST
利用工具为智能体注入强大功能
Earned Nov 3, 2026 EST
Machine Learning Operations (MLOps): Getting Started
Earned Nov 3, 2026 EST
利用 Vertex AI 实现机器学习运维 (MLOps)：模型评估
Earned Nov 3, 2026 EST
Google Security Operations - Deep Dive
Earned Nov 3, 2026 EST
Google DeepMind : 08 Capstone: Develop Your Model for Real-World Impact
Earned Nov 3, 2026 EST
Orchestrate Workflows with the Data Agent Kit
Earned Nov 3, 2026 EST
Customer Experience Agent Studio: Design and Test Solutions
Earned Nov 3, 2026 EST
Google Cloud Agent Governance and Security
Earned Nov 3, 2026 EST
Innovating with Data and Google Cloud
Earned Nov 3, 2026 EST
Google DeepMind: 01 Build Your Own Small Language Model
Earned Nov 3, 2026 EST
Google DeepMind: 02 Represent Your Language Data
Earned Nov 3, 2026 EST
Google DeepMind: 03 Design And Train Neural Networks
Earned Nov 3, 2026 EST
Google DeepMind: 04 Discover The Transformer Architecture
Earned Nov 3, 2026 EST
Google DeepMind: 07 Accelerate Your Model
Earned Nov 3, 2026 EST
Google DeepMind: 05 Fine-Tune Your Model
Earned Nov 3, 2026 EST
管理智能体记忆和状态
Earned Nov 3, 2026 EST
Add Agent Guardrails with Callbacks
Earned Nov 3, 2026 EST
Build a Certification Study Guide: PCA Exam Prep
Earned Nov 3, 2026 EST
部署您的第一个智能体
Earned Nov 3, 2026 EST
Coordinate Multiple Agents
Earned Nov 3, 2026 EST
Agent Assist 及其生成式 AI 功能
Earned Nov 3, 2026 EST
使用生成式 playbook 创建智能体
Earned Nov 3, 2026 EST
AI Boost Bites：打造您的专属复古街机游戏
Earned Oct 16, 2026 EDT
AI Boost Bites：成为“氛围 DJ”
Earned Oct 16, 2026 EDT
AI Boost Bites：打造您的专属大学择校报告
Earned Oct 16, 2026 EDT
AI Boost Bites：一键生成营销活动视觉内容
Earned Oct 16, 2026 EDT
AI Boost Bites：从餐巾纸草图到功能齐全的应用
Earned Oct 16, 2026 EDT
AI Boost Bites: Notes to Sheets with Gemini
Earned Oct 16, 2026 EDT
AI Boost Bites：Gmail 中的 Gemini 日历使用技巧
Earned Oct 16, 2026 EDT
AI Boost Bites: Build Slides Fast with Gemini
Earned Oct 16, 2026 EDT
AI Boost Bites: Talk to Your Data in Sheets
Earned Oct 16, 2026 EDT
AI Boost Bites: AI Magic in a Sheets Cell
Earned Oct 16, 2026 EDT
AI Boost Bites：无代码表格与脚本
Earned Oct 16, 2026 EDT
AI Boost Bites：使用 Gemini 和 Apps 脚本自动执行任务
Earned Oct 16, 2026 EDT
AI Boost Bites: Advanced Analysis in Sheets
Earned Oct 16, 2026 EDT
打造您的首个 Gemini Enterprise 应用
Earned Oct 16, 2026 EDT
企业智能体及其应用场景
Earned Oct 16, 2026 EDT
智能体基础知识
Earned Oct 16, 2026 EDT
生成式 AI 简介
Earned Oct 15, 2026 EDT
本自助式课程将指导学员如何使用 Gemini Notebook 为 Associate Google Workspace Administrator (AGWA) 认证考试创建实用且条理清晰的学习指南。学员将获得实操经验，包括从 Google Workspace 官方文档中提取关键概念、编写量身定制的提示词以生成专属复习题和抽认卡，以及将学习材料整理到专用的 Gemini Notebook 工作区中。
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This course teaches you how to administer Google AppSheet, a no-code app development platform. In this course, you learn how to plan an AppSheet rollout strategy in your enterprise. You also learn how to manage AppSheet teams by using the standard or organizational model, and how to implement governance policies. We discuss how data sources and sample apps can be shared within the enterprise, and the options available for external integration and monitoring with AppSheet.
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Ready to move your databases to Google Cloud? This course provides the strategy. Learn to evaluate business drivers to overcome high on-premise costs and vendor lock-in. Use Migration Center to discover assets, analyze TCO, and build a phased wave plan. You’ll learn to choose the right migration path and identify the right Google Cloud solutions for your workloads. You'll leave with a data-driven business case and a clear roadmap, ready to win executive buy-in and lead a successful migration.
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Learn how to use Gemini Notebook to create a personalized study guide for the Associate Cloud Engineer certification exam.
You'll review Gemini Notebook features, create a notebook in Gemini Notebook , and learn how to use a study guide to practice for a certification exam.
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Learn to leverage Gemini Notebook to build a personalized study guide for the Professional Cloud
Developer (PCD) certification exam. Explore key features, integrate source materials, and use
effective prompts to streamline your exam preparation.
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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Data Engineer certification exam.
You'll review Gemini Notebook features, create a notebook in Gemini Notebook, and learn how to use a study guide to practice for a certification exam.
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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Machine Learning Engineer (PMLE) certification exam. You'll review Gemini Notebook features, create a notebook, and use the study guide to practice for a certification exam.
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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Cloud Database Engineer certification exam. You'll get an overview of the Gemini Notebook essentials, learn how to add sources, and use the study guide to practice for a certification exam.
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本课程为数据库专业人员提供了一套可消除运营孤岛的战略框架。集成了 Gemini 的 Database Center 是一个针对您整个数据库资产的统一指挥控制台；借助该工具，您可以将全托管式服务与部署在 Compute Engine 上的自管数据库整合到一个可观测性信息中心内。本课程将助您掌握 Gemini 辅助的分析洞见功能，实现从被动式问题排查到主动式领导的转型，获得自动执行安全状况管理的能力，强制执行全组织范围的合规性标准，并在性能瓶颈对业务造成冲击之前完成性能优化。
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In the context of a real-world use case, learn how to use Security Command Center’s virtual red teaming feature to identify risks. Then, learn how attack exposure scores help you prioritize issues and how risk reports keep stakeholders in the loop.
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本课程回顾了 Model Armor 的基本安全功能，并让您能够使用该服务。您将了解与 LLM 相关的安全风险，以及 Model Armor 如何保护您的 AI 应用。
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Learn how to use Security Command Center (SCC) to triage vulnerabilities and misconfigurations identified by platform security detectors.
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Google Threat Intelligence 能够向全球各地的安全团队及时提供详细的威胁情报，帮助其更好地监测威胁。本课程将介绍 Google Threat Intelligence 的各项功能，以及各类组织借助该产品主动缓解威胁的常见方式。
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本课程致力于为您提供所需的知识和工具，让您能够了解 MLOps 团队在部署和管理生成式 AI 模型以及探索 Vertex AI 如何帮助 AI 团队简化 MLOps 流程时面临的独特挑战，并帮助您在生成式 AI 项目中取得成功。
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This course introduces the SOAR component of Google SecOps, guiding learners through setup, response automation, and reporting. With demos, hands-on examples, and quizzes, participants will learn how to simplify investigations and make day-to-day security operations more efficient. The course is designed around short videos, averaging about five minutes each, divided into granular topics to support different learning styles and fit into the time learners have available.
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This course provides a comprehensive, end-to-end exploration of Google SecOps SIEM, guiding learners from foundational concepts to advanced investigation and detection engineering. Participants will gain hands-on experience with data ingestion, normalization, RBAC configuration, searching, and dashboarding using both legacy and native capabilities. Through structured modules, demos, and curated examples, the course emphasizes real-world investigation workflows, UDM-based analytics, and YARA-L rule development. By the end of the course, learners will be equipped to operationalize SIEM effectively within their environment and build scalable processes for detection, investigation, and reporting.
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您已经构建了具有高级配置的智能体，现在可赋予它们实际应用能力。为智能体配备能够搜索网络、执行代码、查询数据库和执行自定义操作的工具。让智能体从“智能回答者”转变为能够自主采取行动的得力助手。
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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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本课程能让机器学习从业者掌握评估生成式和预测式 AI 模型的基本工具、方法和最佳实践。要确保机器学习系统在实际运用中提供可靠、准确、高效的结果，做好模型评估至关重要。
学员将深入了解各项评估指标、方法及如何在不同模型类型和任务中适当应用这些指标和方法。课程将着重介绍生成式 AI 模型带来的独特挑战，并提供有效解决这些挑战的策略。通过利用 Google Cloud 的 Vertex AI Platform，学员可学习如何在模型选择、优化和持续监控工作中实施卓有成效的评估流程。
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Take these next steps to learn more about the Google Security Operations Platform. Build on fundamental knowledge to go deeper on customization and tuning.
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In this Google DeepMind course, you will complete a capstone project that brings together the technical knowledge, ethical awareness, and creative problem-solving skills that you have developed throughout the Google DeepMind: AI Research Foundations curriculum. You will apply what you have learned to a problem of your choosing. You will first plan your project to ensure that you are clear on your project aims and intended impact. You will then collect and prepare data for your model. Finally, you will implement a full fine-tuning workflow using Gemma 3 with low-rank adaptation (LoRA) and evaluate its performance. You will receive guidance, but the emphasis will be on adaptation and experimentation - skills that are essential for applied AI research.
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This course brings AI-driven, natural-language data engineering into the IDE and CLI workflows you already use. Built on the Agent Development Kit (ADK), the Data Agent Kit connects to a wide array of Google Cloud services, including BigQuery, Spanner, BigLake, Dataproc, and Managed Airflow. By using it, you can collapse days of pipeline scaffolding, data build tool (dbt) authoring, and orchestration wiring into prompt-driven sessions.
As an intermediate-to-advanced practitioner, you will learn to build a data agent with the Agent Development Kit (ADK). You'll wire up the Model Context Protocol (MCP) layer, and use your agent to explore catalogs, transform data, train models, and orchestrate scheduled pipelines on Google Cloud.
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In this advanced course, you will design, evaluate, deploy, and monitor production-ready AI agents using Customer Experience Agent Studio (CX Agent Studio).
You will apply deterministic design principles, test agents with simulations, and build low-latency voice experiences with Native Audio. You will accelerate development using the CX Agent Studio Model Context Protocol (MCP) server and enterprise connectors. Then, evaluate agent performance with golden test cases and the AI-assisted quality hill climbing process. Finally, deploy your agents across web and telephony channels, configure human escalation, and monitor live conversations using Quality AI in CX Insights.
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As enterprise teams transition to autonomous AI agents, security evolves toward proactive, identity-centric protection. In this course, you will implement a zero-trust security architecture for Cymbal Banking's underwriting platform using the Gemini Enterprise Agent Platform. By deploying a regional Egress Agent Gateway, configuring Model Armor and Semantic Governance (SGP) safety templates, assigning cryptographic SPIFFE identities to agent runtimes, and declaring a VPC-SC perimeter in Terraform, you will protect database boundaries and manage tools with production-grade governance.
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Cloud technology on its own only provides a fraction of the true value to a business; When combined with data–lots and lots of it–it has the power to truly unlock value and create new experiences for customers.
In this course, you'll learn what data is, historical ways companies have used it to make decisions, and why it is so critical for machine learning. This course also introduces learners to technical concepts such as structured and unstructured data. database, data warehouse, and data lakes. It then covers the most common and fastest growing Google Cloud products around data.
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In this Google DeepMind course, you will learn the fundamentals of language models and gain a high-level understanding of the machine learning development pipeline. You will consider the strengths and limitations of traditional n-gram models and advanced transformer models. Practical coding labs will enable you to develop insights into how machine learning models work and how they can be used to generate text and identify patterns in language. Through real-world case studies, you will build an understanding around how research engineers operate. Drawing on these insights you will identify problems that you wish to tackle in your own community and consider how to leverage the power of machine learning responsibly to address these problems within a global and local context.
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In this Google DeepMind course you will learn how to prepare text data for language models to process. You will investigate the tools and techniques used to prepare, structure, and represent text data for language models, with a focus on tokenization and embeddings. You will be encouraged to think critically about the decisions behind data preparation, and what biases within the data may be introduced into models. You will analyze trade-offs, learn how to work with vectors and matrices, how meaning is represented in language models. Finally, you will practice designing a dataset ethically using the Data Cards process, ensuring transparency, accountability, and respect for community values in AI development.
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In this Google DeepMind course you will focus on the training process for machine learning models. You will learn how to spot and mitigate issues when training a model, such as overfitting and underfitting. In practical coding labs, you will implement and evaluate the multilayer perceptron for simple classification tasks. This will provide insights into the mechanics of training a neural network model and the backpropagation algorithm. Research case studies will demonstrate how neural networks power real-world models. Additionally, you will consider the broader social impacts of innovation by looking beyond immediate benefits to anticipate potential risks, safety concerns, and further-reaching societal consequences.
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In this Google DeepMind course you will discover the mechanisms of the transformer architecture. You will investigate how transformer language models process prompts to make context-sensitive next-token predictions. Through practical activities you will explore the attention mechanism, visualize attention weights, and encounter advanced concepts like masked attention and multi-head attention. You will also learn other techniques that are necessary to build neural networks that are well-suited to be used as language models. Finally, through activities on values, stakeholder mapping and community engagement, you will practice concrete tools for ensuring AI projects are developed with communities, not just for them.
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Train more powerful models with a single GPU. In this course, you will learn how hardware can speed up model training and the key considerations when training models on a GPU. First, you will learn how to estimate the number of computations and the amount of computer memory required to train large neural networks. You will then discover techniques for reducing the computing and memory requirements when training a model. Techniques which you will apply for fine-tuning a Gemma model with 4 billion parameters. Finally, you will consider the potential environmental impacts of machine learning, with a focus on where questions of energy, water, and e-waste intersect with justice and equity.
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Unleash the power of language models with fine-tuning. In this course, you will learn how to adjust a pre-trained model to a specific task. You will start with full-parameter fine-tuning using a small language model. To tune larger models like Gemma, you will learn parameter-efficient techniques with a focus on LoRA. Finally, you will be briefly introduced to reinforcement learning as an alternative to supervised fine-tuning (SFT). You will also explore how AI is imagined and made sense of in cultural contexts. You will consider why responsible AI is not just about technical safety but also about building governance systems that reflect community values and protect the public interest.
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您已经构建了能够回答查询的基本 LLM 智能体，现在让我们为其赋予状态。使用会话状态来构建能够维护上下文、记住用户偏好并提供个性化体验的智能体。将智能体从“无状态的回复者”转变为“智能助理”。
探索 Gemini Enterprise Agent Ready (GEAR) 计划中的其他内容。
加入社区论坛，提出问题并参与讨论
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You’ve built capable agents with powerful tools—now ensure they operate safely and reliably. Use callbacks as observation points and control mechanisms. Monitor what’s happening, validate inputs and outputs, implement guardrails, and gain confidence that your agents behave in production.
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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Cloud Architect certification exam.
You'll review Gemini Notebook features, create a notebook in Gemini Notebook, and learn how to use a study guide to practice for a certification exam.
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将智能体从 localhost 部署到生产环境。本课程将介绍如何将 ADK 智能体部署到 Vertex AI Agent Engine 和 Cloud Run，以及如何通过记忆库实现可选的跨会话记忆功能。
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Learn to coordinate multiple specialized agents working together. This lesson teaches you when to use multi-agent systems, how to orchestrate agents with workflow patterns, and how agents communicate through shared state. By the end, you’ll build a complete multi-agent application.
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您可以利用強大的生成式 AI 打造智慧型自動化代理。完成本課程後，您將瞭解如何開發資料儲存庫代理，自動從網站、文件或結構化資料中擷取並彙整資訊，即時回答複雜問題。這種新型代理能提供靈活、準確的答案，還會附上原始來源網址，而且設定相當便捷，讓您從此告別靜態常見問題集。
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在本课程中，您将学习如何利用生成式 playbook 为 Conversational Agents 构建出色的对话体验。您首先会了解 playbook 的大致情况，然后学习如何设置您的首个 playbook。您还将了解测试的重要性，以及配额限制和集成等关键生产注意事项。课程最后将通过一个案例研究展示如何利用 playbook 改变内容生成方向。
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本视频介绍如何向 Gemini Canvas 输入一条简短的提示词，即可打造、定制并分享专属的可玩游戏（例如井字棋）。
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本视频介绍如何在 Gemini Canvas 中使用简单的文本提示词，打造一个配备自定义控件和可弹奏键盘的全交互式音乐合成器。
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本视频介绍了如何使用 Gemini Deep Research 和创意提示词，将复杂信息转化为有趣的个性化分析，例如体育球探报告和对阵图。
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本视频介绍了如何使用 Gemini Canvas 中的“创建”菜单，将大段文字立即转换为信息图和网页等视觉内容。
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本视频介绍了如何使用 Gemini Canvas，通过一种名为“氛围编程”的技术，将简单的应用绘图转换为功能原型。
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In this video, you'll learn to use Gemini in Google Sheets to automatically generate organized trackers from unstructured data. You'll learn how to write a prompt that references other Google Drive files using the "@" symbol. You'll also see how Gemini builds a fully formatted, color-coded table with drop-down menus based on the data in your notes, saving you significant manual effort.
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本视频介绍如何使用 Gmail 中的 Gemini 来管理 Google 日历，包括在侧边栏中询问日程，以及使用一键“添加到日历”功能将邮件中检测到的活动添加到日历。
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In this video, you'll learn to use Gemini in Google Slides to build polished presentations efficiently. You'll see how to create slides by referencing content directly from your Google Drive files using the "@" symbol. You'll also learn to generate custom, professional images from a simple text prompt to visually enhance your presentation.
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In this video, you'll learn to use the "Ask Gemini" feature in Google Sheets to manage your data with natural language. You'll see how to ask Gemini to create dropdowns, highlight sales data, create a pivot table, filter by month, and sort by revenue.
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In this video, you'll learn to use the =AI() function in Google Sheets to automate your work. You'll see how to generate text like slogans, summarize paragraphs of customer feedback, and categorize data by classifying inquiries and analyzing sentiment.
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本视频将介绍如何借助 Google 表格中 Gemini 强大的 AI 功能，在数分钟内轻松提取数据并生成分析洞见，完全无需任何技术或编程背景。
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本视频介绍如何借助 Gemini 与 Apps 脚本，实现跨 Google Workspace 各项手动任务的自动化。您将学习如何编写提示词让 Gemini 生成 Apps 脚本代码，从而在 Google 表格中自动为未标记为“完成”的任务起草邮件提醒。几乎无需任何技术专业背景，即可实现工作流自动化，从而腾出时间专注于更重要的工作，彻底告别繁琐的手动跟进。
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In this video, you'll learn to use Gemini in Google Sheets for advanced data analysis. You'll learn how to create data visualizations, like a scatter plot, using a simple prompt. You'll also learn how to ask Gemini to analyze your data for strategic insights and recommendations, such as how to save money.
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打造您的首个 Gemini Enterprise 应用，赢得技能徽章！将各种数据源连接到您的应用中，构建强大、统一的搜索和分析引擎。掌握进阶能力，如：深度研究型智能体、多智能体协同构思，以及用于进行聚焦式分析的 NotebookLM。
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了解 AI 智能体如何为业务带来实际影响。您将把智能体类型关联到您的关键绩效指标 (KPI)，并探索能够解决实际瓶颈的用例。然后，您将了解 Gemini Enterprise 如何帮助您构建和编排合适的智能体，其范围涵盖从无代码到高代码的各种解决方案。
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本课程将介绍 AI 智能体的基础知识，并探讨智能体在现实世界中的价值所在。它为想要从自主、目标导向行为的角度理解 AI 系统的开发者、架构师和技术决策者奠定了基础。
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这是一节入门级微课程，旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。
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