Mastering Azure Machine Learning Service by Neil Hambly

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Mastering Azure Machine Learning Service by Neil Hambly

$349.00 $159.00

Mastering Azure Machine Learning Service

Abstract: Putting a successful Data Science project together is more than executing different tools and processes.

It requires multiple people working as a team, working through the phases in the right sequence, constantly evaluating and trying new techniques until we find the right

For this we need a scalable environment with access to the range of Machine Learning resources that use to pin it all together.

Using Azure Machine Learning Service to provide the tools and environment in which we can execute our experiments, integrate with other Microsoft resources like DevOps, Security and Visualizations.

Modules:

    1. Introduction to Azure Machine Learning Service.
    2. Preparing data for Machine Learning.
    3. Compute Resources.
    4. Choosing Algorithms.
    5. Using Designer for no-code solutions.
    6. Using Python Notebooks.
    7. Explaining Machine Learning Visuals.
    8. Deploying Machine Learning pipelines

PS: This class will be recorded and the registered attendee will get 12 months streaming access to the recorded class. The recordings will be available within 30 days of class completion.

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Description

Mastering Azure Machine Learning Service

Abstract: Putting a successful Data Science project together is more than executing different tools and processes.

It requires multiple people working as a team, working through the phases in the right sequence, constantly evaluating and trying new techniques until we find the right

For this we need a scalable environment with access to the range of Machine Learning resources that use to pin it all together.

Using Azure Machine Learning Service to provide the tools and environment in which we can execute our experiments, integrate with other Microsoft resources like DevOps, Security and Visualizations.

Modules:

    1. Introduction to Azure Machine Learning Service.
    2. Preparing data for Machine Learning.
    3. Compute Resources.
    4. Choosing Algorithms.
    5. Using Designer for no-code solutions.
    6. Using Python Notebooks.
    7. Explaining Machine Learning Visuals.
    8. Deploying Machine Learning pipelines

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