The Exam Prep DP-100: Microsoft Certified Azure Data Scientist Associate course is designed for professionals aiming to apply data science and machine learning to Azure workloads. This course equips learners with the skills to design, implement, and optimize machine learning solutions using Azure Machine Learning, MLflow, and Azure AI services. Participants will gain hands-on experience in data ingestion, preparation, model training, deployment, and monitoring. Through practical demonstrations and real-world scenarios, the course ensures learners are prepared to build scalable AI solutions in Azure.

Azure ML: Explore & Configure the Machine Learning Workspace
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您将获得的技能
- Data Management
- Data Preprocessing
- Microsoft Azure
- MLOps (Machine Learning Operations)
- Model Deployment
- Statistical Modeling
- Model Evaluation
- Statistical Methods
- Development Environment
- Data Science
- Cloud Computing
- Artificial Intelligence and Machine Learning (AI/ML)
- Azure Synapse Analytics
- Machine Learning
- 技能部分已折叠。显示 9 项技能,共 14 项。
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4 项作业
了解顶级公司的员工如何掌握热门技能

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该课程共有2个模块
This module provides a comprehensive foundation in data science and machine learning, equipping learners with the essential knowledge required for the DP-100 certification. Participants will explore key concepts such as data science processes, machine learning fundamentals, and statistical modeling, ensuring a strong grasp of core principles. The module covers different types of machine learning, common terminologies, and data aspects relevant to model development. Learners will also gain insights into best practices for selecting and managing machine learning solutions, including preprocessing techniques and evaluation methodologies. By the end of this module, participants will develop a structured understanding of data science workflows, enabling them to apply these skills effectively in real-world scenarios and certification preparation
涵盖的内容
11个视频2篇阅读材料2个作业1个讨论话题
This module provides a comprehensive understanding of compute and data management within Azure Machine Learning, equipping learners with the skills to efficiently configure and optimize ML workflows. Participants will explore key concepts such as creating and managing compute instances, clusters, and attached computes within the Azure ML workspace. The module covers CPU vs. GPU selection for different workloads, datastore and data asset management, and leveraging Uniform Resource Identifiers (URIs) for resource identification. Learners will gain expertise in configuring environments, integrating Synapse Spark pools, and training machine learning models with Azure ML. Additionally, the module includes valuable exam tips to ensure learners are well-prepared for certification. By the end of this module, participants will be equipped with practical knowledge to manage compute and data resources effectively within Azure ML for scalable AI solutions
涵盖的内容
12个视频1篇阅读材料2个作业
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