课程
Gen AI: Beyond the Chatbot
- 基础技能水平
- 4.8+
- 226 条评价
This course aims to move beyond the basic understanding of chatbots to explore the true potential of generative AI for your organization.
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观看专家讲师的短视频,然后在浏览器中通过互动练习实践所学内容。
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This course aims to move beyond the basic understanding of chatbots to explore the true potential of generative AI for your organization.
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Create and refine videos faster with Gemini in Google Vids. Use AI-powered storyboarding and content generation to produce polished videos with ease.
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This course helps your preparation for the Associate Cloud Engineer exam, learn about the Google Cloud domains in the exam and create a study plan.
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Gain an overview of AI Agents. Discover how AI Agents use autonomous action and reasoning to solve complex problems.
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This course provides an overview of the opportunities and challenges companies encounter in their digital transformation journey.
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Learn AI agent fundamentals — how they differ from LLMs, when to use them, and explore agent architecture, orchestration, and tools.
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This course explores how organizations can use custom gen AI agents to help tackle specific business challenges.
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Prepare for the Google Cloud Professional Data Engineer certification with diagnostic questions covering migration, storage, analytics, and automation.
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You unlock the foundational concepts of generative AI by exploring the differences between AI, ML, and gen AI.
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This course introduces Google’s gen AI applications, such as Google Workspace with Gemini and NotebookLM.
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You explore the different layers of building gen AI solutions, Google Cloud’s offerings, and the factors to consider when selecting a solution.
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In this Google DeepMind course, you will learn the fundamentals of language models and gain a high-level of machine learning development pipelines.
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Learn to use Googles Agent Development Kit (ADK) to build complex, production-ready AI agents with a code-first, structured development approach.
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Exploring Data Transformation with Google Cloud
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Connect data sources to your app to build a search and analysis engine. Master capabilities like deep research agents, ideation, and NotebookLM for analysis.
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Explore multi-agent system architecture and deployment using Googles ADK and Google Cloud infrastructure for production-grade agent applications.
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Learn the data engineering role on Google Cloud. Explore data sources, storage solutions, ETL/ELT architectures, BigQuery, Dataform, and Dataproc.
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Map agent types to your KPIs and explore use cases that solve problems, learn how Gemini Enterprise empowers you to build and orchestrate the right agents.
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This course introduces the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Infrastructure Foundations.
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Build, configure, and run your first AI agent using Googles Agent Development Kit (ADK). Set up environments, create agents in Python and YAML.
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Modernize Infrastructure and Applications with Google Cloud
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Learn human-centric AI orchestration. Distinguish between augmentation and automation, and balance machine efficiency with human intuition.
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Deploy ADK agents to production using Vertex AI Agent Engine and Cloud Run. Add persistent cross-session memory with Memory Bank.
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Learn Google Cloud essentials including computing, storage, networking, and resource management through videos and hands-on labs in this foundational course.
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This course introduces Google Clouds AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects.
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In this Google DeepMind course you will discover the mechanisms of the transformer architecture.
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This course introduces the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Core Services.
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数据科学是一个专注于从数据中获取信息的专业领域。数据科学家使用编程技能、科学方法、算法等来分析数据,形成可操作的洞察。
您需要学习 Python 或 R 等编程语言,掌握数学和统计学原理。数据分析方法和数据科学工具的知识也是必不可少的。学习数据科学有很多方法。除了正式的教育途径,如学位或大学学习,还有很多其他资源可以帮助您按自己的节奏学习。除了在线课程和教程,还有书籍、视频等。
除了数学和统计学知识,数据科学家还需要 Python、R 和 SQL 等语言的编程技能。此外,数据科学需要处理大型数据集的能力、数据可视化、数据整理和数据库管理知识。机器学习和深度学习技能也很有用。
在专业领域,几乎每个行业都可以在某种程度上使用数据科学。医疗机构使用数据科学来检测和治疗疾病,金融公司用它来检测和预防欺诈。各种行业都将数据科学用于营销,如构建推荐系统和分析客户流失。
是的,数据科学是美国和全球增长最快的行业之一。它也是薪酬最高的职业之一。根据 Payscale 的数据,在美国,有经验的数据科学家平均收入为 97,609 美元,满意度评分为五星中的四星。
这里有几个需要考虑的因素。首先,数据科学学位的竞争可能很激烈,通常需要持续的高分。同样,数据科学所需的许多技能需要大量的学习和耐心。掌握所有必要的基础知识可能需要几个月的时间,还需要大量的实践经验才能获得入门级职位。
是的,您需要一些 Python、R、SQL、Java 和 C/C++ 等语言的编程经验。不过,由于语法相对简单,Python 编程语言通常是新手的首选。
对于没有编程经验和/或数学背景的人来说,通常需要 7 到 12 个月的密集学习才能达到入门级数据科学家的水平。但是,重要的是要记住,仅仅学习数据科学的理论基础可能不会让您成为真正的数据科学家。
掌握数据科学基础后,您可以专攻各种领域,包括机器学习、人工智能、大数据分析、商业分析和智能、数据挖掘等。
随时随地通过我们的移动课程和每日 5 分钟编程挑战提升技能。