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Azure ML: Designing and Preparing Machine Learning Solutions
Whizlabs

Azure ML: Designing and Preparing Machine Learning Solutions

包含在 Coursera Plus

深入了解一个主题并学习基础知识。
中级 等级

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9 小时 完成
灵活的计划
自行安排学习进度
深入了解一个主题并学习基础知识。
中级 等级

推荐体验

9 小时 完成
灵活的计划
自行安排学习进度

要了解的详细信息

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作业

6 项作业

授课语言:英语(English)

了解顶级公司的员工如何掌握热门技能

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 Exam Prep DP-100: Microsoft Azure Data Scientist Associate 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
  • 获得对主题或工具的基础理解
  • 通过实践项目培养工作相关技能
  • 获得可共享的职业证书

该课程共有3个模块

This course provides a comprehensive understanding of data science and machine learning, focusing on essential concepts and their applications. It emphasizes the fundamental principles of data analysis, statistical modeling, and machine learning techniques, fostering a strong foundation for practical implementation. Participants will gain valuable insights into different types of machine learning, real-world use cases, and best practices for selecting appropriate models. The course also covers key ML terminology, data preprocessing, and the statistical foundations necessary for building robust solutions, preparing learners for both theoretical evaluation and hands-on projects.

涵盖的内容

12个视频2篇阅读材料2个作业1个讨论话题

This course provides an in-depth understanding of managing and utilizing datasets within Azure ML workflows using Azure Data Factory and Synapse Analytics. It emphasizes the principles of configuring and managing Azure Machine Learning environments through the CLI and SDK (v2), ensuring seamless integration and automation. Participants will explore techniques for sharing assets across workspaces, optimizing scalability with registries, and designing efficient ML workflows. Additionally, the course delves into monitoring, retraining, and scaling ML models using Apache Spark and MLOps practices, reinforcing best practices for lifecycle management in production environments.

涵盖的内容

7个视频1篇阅读材料2个作业

This course provides a deep dive into identifying appropriate data sources, formats, and ingestion strategies for machine learning projects in Azure, ensuring efficient data handling. It emphasizes the principles of selecting the right services and compute options for model training, optimizing performance and scalability. Participants will gain expertise in differentiating between real-time and batch deployment strategies based on consumption needs, enabling informed architectural decisions. Additionally, the course explores MLOps best practices, guiding learners through the design and implementation of scalable workflows and effective Azure ML environment organization, ensuring seamless integration and lifecycle management.

涵盖的内容

12个视频1篇阅读材料2个作业

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