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DP-100: Azure Machine Learning & Data Science Exam Prep 2023

DP-100: Azure Machine Learning & Data Science Exam Prep 2023

Azure Machine Learning, AzureML, Exam DP-100: Designing and Implementing a Data Science Solution, 4 End-to-End Projects

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What you'll learn

  • Prepare for DP-100 Exam
  • Getting Started with Azure ML
  • Setting up Azure Machine Learning Workspace
  • Running Experiments and Training Models
  • Deploying the Models
  • AzureML Designer: Data Preprocessing
  • Regression Using AzureML Designer
  • Classification Using AzureML Designer
  • AzureML SDK: Setting up Azure ML Workspace
  • AzureML SDK: Running Experiments and Training Models
  • Use Automated ML to Create Optimal Models
  • Tune hyperparameters with Azure Machine Learning
  • Use model explainers to interpret models
  • Model Registration and Deployment Using Azureml SDK

Machine Learning and Data Science are one of the hottest tech fields nowadays! There are a lot of opportunities in these fields. Data Science and Machine Learning have applications in almost every field, like transportation, Finance, Banking, Healthcare, Defense, Entertainment, etc.

Most professionals and students learn Data Science and Machine Learning but specifically, they are facing difficulties while working in a cloud environment. To solve this problem I have created this course, DP-100. It will help you to apply your data skills in Azure Cloud smoothly.

This course will help you to pass the "Exam DP-100: Designing and Implementing a Data Science Solution on Azure". In this course, you will understand what to expect on the exam and it includes all the topics that are required to pass the DP-100 Exam.

Below are the skills measured in DP-100 Exam,

1) Design and prepare a machine learning solution (20–25%)

  • Design a machine learning solution
  • Manage an Azure Machine Learning workspace
  • Manage data in an Azure Machine Learning workspace
  • Manage compute for experiments in Azure Machine Learning

2) Explore data and train models (35–40%)

  • Create models by using the Azure Machine Learning designer
  • Explore data by using data assets and data stores
  • Create models by using the Azure Machine Learning designer
  • Use automated machine learning to explore optimal models
  • Use notebooks for custom model training
  • Tune hyperparameters with Azure Machine Learning

3) Prepare a model for deployment (20–25%)

  • Run model training scripts
  • Implement training pipelines
  • Manage models in Azure Machine Learning

4) Deploy and retrain a model (10–15%)

  • Deploy a model
  • Apply machine learning operations (MLOps) practices

So what are you waiting for, Enroll Now and understand Azure Machine Learning to advance your career and increase your knowledge!