Azure Machine Learning: 1-Day Executive Workshop

WaveAccess2

Hands-on experience in Machine Learning using Azure, understanding what a ML team does — and how to evaluate their results.

This workshop provides a hands-on experience in machine learning using Azure and helps you understand what a ML team does - and how to evaluate their results.

The workshop provides an overview of typical ML tasks, regression and classification. We will also introduce overfitting, regularization and cross-validation, and explain how to interpret results. The interactive part includes writing Python code and creating models in Azure ML Studio: you will apply a regression algorithm to a real dataset, see the outputs, and find out what they actually mean!

To run the analysis, we will employ Microsoft Azure Notebooks free service, available from any browser, that allows to develop and run Jupyter Notebooks in the cloud. The models will be created and analyzed in Azure ML Studio. Data for the workshop would be downloaded from a datastore on Microsoft Azure Cloud.

As a result, you will get an understanding of a typical ML workflow and be able to supervise your ML team in order to maximize the value of Machine Learning projects in Azure.

This workshop is for:

  • Managers who consider introducing ML to their business
  • Tech executives (CTO, CIO, etc.)
  • Tech leads

Outline:

  1. Linear regression
  2. Regression metrics
  3. Polynomial regression
  4. Overfitting and Cross-Validation
  5. Example of applying regression to real data in Azure ML Studio
  6. Classification
  7. Сlassification metrics
  8. Applying classification to a classical beginner dataset in Azure ML Studio

This workshop is available on-premise or online, by arrangement.

Price given is per person for on-site training, excluding travel costs. A minimum number of participants is required for on-site training.

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https://store-images.s-microsoft.com/image/apps.62974.b7c5012c-425b-4565-9573-5b3b36c35d2e.f18dacb6-efda-4b56-9cc3-c3b80567cc0c.008877b5-9cfa-4a45-be06-75f2283c6862