DP-100 Microsoft Azure Data Scientist Complete Exam Prep
About this course
Covers the requirements of the updated DP-100 Exam, Designing and Implementing a Data Science Solution on Azure.Updated as of Dec 2022.This course goes through the requirements of the Microsoft DP-100 exam, section by section. We cover everything you need to know to pass the exam. The Azure Data Scientist applies their knowledge of data science and machine learning to implement and run machine learning workloads on Azure; in particular, using Azure Machine Learning Service. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.This exam measures your ability to accomplish the following technical tasks: set up an Azure Machine Learning workspace; run experiments and train models; optimize and manage models; and, deploy and consume models.Design and prepare a machine learning solution (20–25%)Explore data and train models (35–40%)Prepare a model for deployment (20–25%)Deploy and retrain a model (10–15%)Taught by Scott Duffy, the number one instructor of Microsoft Azure on Udemy.You get lifetime access to the course, and so there are no silly "30-day" countdowns that require you to pay more to extend access. This course will be here when you need it.Enroll today!Microsoft, Windows, and Microsoft Azure are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries. This course is not certified, accredited, affiliated with, nor endorsed by Microsoft Corporation.
69/100
CourseAsk score
- What the provider tells you
- 45/45
- Who stands behind it
- 8/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- set up an Azure Machine Learning workspace
- run experiments and train models
- optimize and manage models
- prepare a model for deployment
- deploy and retrain models
Course objectives
- develop a machine learning solution
- explore data and train models
- manage and optimize machine learning models
- deploy deployed models into production
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