DP-100 Microsoft Azure Data Scientist (DS) Exam Preparation

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Learn to create, deploy, and optimize machine learning models with Azure Machine Learning—hands-on and industry-focused!

1 - Introduction

1 -Introduction to Course

2 -Exam Requirements

2 - Create an Azure Machine Learning Workspace

1 -Create an Azure Machine Learning Workspace

2 -Azure ML Workspace Settings - Portal

3 -Azure ML Studio Settings

3 - Azure Learning Workspace

1 -Data Stores and Datasets

2 -Create Additional Datasets

4 - Manage Experiment Commute Context

1 -Create an Experiment Compute Instance

2 -Manage Multiple Compute Instances

3 -Create Compute Targets and Clusters

5 - Using Azure Machine Learning

1 -Creating our First ML Pipeline

2 -Submitting Pipeline

3 -Custom Code in Pipeline

4 -Understanding Complicated Pipeline

5 -Evaluating Execution Results

6 -Errors in Azure ML Designer

7 -Various Modules of Azure ML Designer

6 - Azure Machine Learning Experience

1 -Setup SDK

2 -Create ML Workspace using SDK

3 -Simple Program in Python

7 - Run Training in an Azure Machine Learning

1 -Train Model using SDK

2 -Submit Experiment using SDK

3 -Create a Pipeline by using SDK

8 - Automate ML to create optimal models

1 -AutoML Overview

2 -AutoML with SDK

9 - Use Hyperdrive to Tune Hyperparameters

1 -Understanding what is Hyperdrive

2 -Register a Trained Model

3 -Create Production Compute Targets

10 - Deploy Model as Service

1 -Deploy AutoML

2 -Create an AutoML Endpoint

3 -Deploy ML Designer for Real Time

4 -Deploy SDK Models

5 -Publish a Pipeline for Batch Inference

11 - Conclusion

1 -Conclusion

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تولید کننده: Udemy