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
Requirements
Basic Understanding of Machine Learning
A Free or Paid Subscription to Microsoft Azure
Description
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!
Who this course is for
Anyone who wants to learn Azure Machine Learning
Students and Professionals Who Wants to Pass DP-100 Exam


Homepage

https://www.udemy.com/course/dp-100-azure-machine-learning-data-science-for-beginners