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所在平台: Udemy |
课程主页: https://www.udemy.com/course/google-cloud-professional-machine-learning-engineer-questions/
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**课程名称：** Google Cloud 专业机器学习工程师 **课程概述：** 本课程旨在帮助学员为 Google Cloud 专业机器学习工程师认证做好准备，重点关注使用 Google Cloud Platform (GCP) 设计、构建和部署机器学习模型的实用技能。课程涵盖了完整的机器学习生命周期——从数据准备和建模到运营和监控——同时强调安全性、合规性和负责任的 AI 实践。 **您将学到：** * 利用 Vertex AI、BigQuery 和 AutoML 等 GCP 工具设计机器学习解决方案。 * 为训练和评估准备和处理结构化和非结构化数据。 * 在生产环境中训练、测试、部署和监控机器学习模型。 * 应用负责任的 AI 原则，包括模型公平性、可解释性和数据隐私。 **课程要求：** * 扎实的 Python 基础和机器学习概念。 * 熟悉 TensorFlow 或 scikit-learn 会有所帮助。 * 建议具备使用云服务（尤其是 Google Cloud）的经验。 * 需要访问 Google Cloud 账户，用于动手实验和练习。 **适合人群：** * 准备考取 Google Cloud 专业机器学习工程师认证的学员。 * 在基于云的解决方案上工作的 数据科学家、机器学习工程师和 AI 专家。 * 将机器学习模型集成到应用程序中的软件工程师和开发人员。 * 希望验证其在 GCP 上构建可伸缩、生产就绪的机器学习管道能力的专业人士。 **课程特色：** 本课程与 Google 的考试指南一致，包含真实世界的案例研究、最佳实践和动手实验，模拟生产环境中机器学习工程师执行的任务。
Google Cloud Professional Machine Learning EngineerCourse DescriptionThis course prepares learners for the Google Cloud Professional Machine Learning Engineer certification by focusing on the practical skills needed to design, build, and deploy machine learning models using Google Cloud Platform (GCP). The course covers the full ML lifecycle-from data preparation and modeling to operationalization and monitoring-while emphasizing security, compliance, and responsible AI practices.What You'll LearnDesign ML solutions using GCP tools like Vertex AI, BigQuery, and AutoMLPrepare and process structured and unstructured data for training and evaluationTrain, test, deploy, and monitor ML models in production environmentsApply responsible AI principles including model fairness, explainability, and data privacyRequirementsSolid understanding of Python and basic machine learning conceptsFamiliarity with TensorFlow or scikit-learn is helpfulExperience working with cloud services, especially Google Cloud, is recommendedAccess to a Google Cloud account for hands-on labs and exercisesWho This Course Is ForIndividuals preparing for the Google Cloud Professional Machine Learning Engineer certificationData scientists, ML engineers, and AI specialists working on cloud-based solutionsSoftware engineers and developers integrating ML models into applicationsProfessionals seeking to validate their ability to build scalable, production-ready ML pipelines on GCPThis course aligns with Google's exam guide and includes real-world case studies, best practices, and hands-on labs that simulate tasks performed by ML engineers in production settings.
