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Getting Started with Google Gemini API

Coursera PlusMonthly 3 个月课程4 折优惠 ，让你轻松掌握闪耀技能。立即节省

Getting Started with Google Gemini API

本课程是 Gemini for Developers 专项课程的一部分

位教师：Google DeepMind

2,550 人已注册

包含在中

了解更多

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Set up environments, manage API keys, choose models, debug prompts, integrate real-time data, and generate structured outputs with JSON Schema.

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

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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 Gemini for Developers 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
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获得可共享的职业证书

该课程共有3个模块

This course introduces Google Gemini API. You’ll move beyond basic chat interfaces to building intelligent, high-performance systems. You will use foundational API setup and progress to sophisticated features like function calling and structured output. You’ll use decision-making to balance cost and speed using Gemini Pro and Flash models. By leveraging Gemini’s unique "thinking" capabilities and web-grounding tools, you will learn to build reliable, transparent AI solutions that process data with precision at scale.

By the end of this course, you will be able to: - Manage API keys and set up development environments in Python or JavaScript. - Choose between models based on cost, latency, and performance requirements. - Use "thinking" and thought summaries to debug prompts and improve transparency. - Integrate real-time data using built-in tools like Google Search and URL Context. - Use JSON Schema to produce consistent, parseable outputs for downstream logic.

单元详情

This foundational module takes you from zero to making your first successful Gemini API call. You'll start by understanding why AI-powered text generation is transforming applications across industries, then dive into the essential concepts every developer needs to know: tokens, context windows, rate limits, and authentication. Through hands-on coding sessions, you'll set up your development environment, secure your API keys, and write your first text generation requests using either Python or JavaScript. By the end of this module, you'll have built a working text generation application and designed three unique use cases for different domains. This module establishes the foundation for all subsequent API work and gives you the confidence to integrate AI text generation into your projects.

涵盖的内容

4个视频1篇阅读材料2个作业

4个视频总计12分钟

Welcome to the Google Gemini for developers specialization2分钟
Introduction to getting started with the Gemini API2分钟
Power up your applications with AI2分钟
Your first Gemini API call5分钟

1篇阅读材料总计10分钟

Gemini API fundamentals10分钟

2个作业总计45分钟

Build an AI text generator30分钟
Knowledge check: Gemini fundamentals15分钟

This module teaches you how to make informed decisions about AI model selection and gain transparency into how your AI thinks. You'll learn the key differences between Gemini Pro and Flash models, understanding when to prioritize quality versus speed and cost. The module then introduces Gemini's unique "thinking" feature, which exposes the model's internal reasoning process. You'll learn to enable thinking, configure thinking budgets, and interpret thought summaries to debug issues and improve your AI's performance. Through practical exercises, you'll build systems that intelligently choose the right model for each task and leverage thinking insights to create more reliable AI applications. This module transforms you from making basic API calls to making strategic, informed decisions about AI behavior.

涵盖的内容

2个视频1篇阅读材料2个作业

This module unlocks Gemini's powerful built-in capabilities and teaches you to format AI responses for reliable downstream processing. You'll master two essential built-in tools: URL Context for analyzing web content and Google Search for accessing real-time information. The module then focuses on structured output using JSON Schema, showing you how to transform messy, unpredictable AI responses into clean, consistent data structures. You'll discover why this combination is crucial for building AI agents—tools provide dynamic information while structured output ensures that information is actionable and parseable by other systems. Through hands-on projects, you'll build information processing systems that combine web data, search results, and structured formatting to create agent-ready outputs.