Building, Training, and Validating Models in Microsoft Azure

سرفصل های دوره

This course gives Microsoft Azure Data Scientists a road map on how to build, train, and validate machine learning models in Azure.

1. Course Overview

1. Course Overview

2. Creating a Hypothesis

1. Overview

2. Factors of a Well Defined Hypothesis

3. Alternative Hypothesis and Null Hypothesis

4. Key Performance Indicators Required

5. Hypothesis Acceptance Criteria

6. Summary

3. Sourcing and Transforming Data Relevant to a Hypothesis

1. Overview

2. Azure Machine Learning Services Overview

3. Identifying and Sourcing the Relevant Data

4. Training and Testing Data Subsets

5. Data Transformation Using Azure Machine Lear

6. Summary

4. Identifying Features from Raw Data

1. Overview

3. Dependent and Independent Variables

4. Choosing the Right Encoding Method

5. Feature Scaling

6. Common Feature Selection Techniques

7. Summary

Selecting Data Granularity

5. Building the Model

1. Overview

2. Selecting the Appropriate Model

3. Azure ML - Classification Model Options

4. Building and Registering the Model

5. Metrics for Model Classification Optional

6. Tuning Hyperparameters and Interpreting the Model

7. Deploying the Model

8. Summary

6. Monitoring and Managing the Performance of a Model

1. Overview

2. Model Performance Strategy

3. Retraining Model with Automated Machine Learning

4. Monitoring the Model

5. Monitoring Data Drift

6. Summary

139,000 تومان

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تولید کننده: PluralSight