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Azure Machine Learning Course

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Overview:

This 12-week course provides an accessible introduction to Azure Machine Learning Studio, a powerful cloud-based environment for constructing and deploying machine learning solutions. Students will learn how to navigate the platform, build models using drag-and-drop interfaces, and understand the key concepts of machine learning and data science applicable in Azure.

What You'll Learn

Fundamentals of Azure Machine Learning Studio and its interface.
Basic concepts and workflows of machine learning.
How to prepare data, build, and train models using Azure ML Studio.
Deploying and managing models within Azure.
Ethical considerations and best practices in machine learning deployment.

Course Content

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Module 1: Getting Started with Azure ML Studio

Lesson 1: Introduction to Azure Machine Learning Studio 3 Topics

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Overview of Azure ML Studio capabilities and tools.

Setting up an Azure ML Studio account.

Navigating the Azure ML Studio workspace.

Lesson 2: Data Science and Machine Learning Basics 3 Topics

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Basic concepts in data science and machine learning.

Overview of supervised vs. unsupervised learning.

Introduction to classification, regression, and clustering.

Lesson 3: Data Management in Azure ML Studio 3 Topics

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Uploading and managing data sets.

Using data transformation modules in Azure ML.

Data cleaning and preprocessing techniques.

Module 2: Building Machine Learning Models

Lesson 4: Model Building Basics 3 Topics

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Using the drag-and-drop interface to build models.

Selecting algorithms for different types of data and objectives.

Configuring data splits and training settings.

Lesson 5: Training and Evaluating Models 3 Topics

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Running training experiments.

Evaluating model accuracy and performance metrics.

Understanding overfitting and how to mitigate it.

Lesson 6: Improving Model Performance 3 Topics

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Feature selection and feature engineering techniques.

Using hyper parameter tuning in Azure ML Studio.

Implementing cross-validation workflows.

Module 3: Deploying and Consuming Models

Lesson 7: Model Deployment 3 Topics

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Preparing a model for deployment.

Deploying models as web services.

Securing and managing access to deployed models.

Lesson 8: Consuming Deployed Models 3 Topics

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Consuming models via web services.

Integrating Azure ML models with applications.

Monitoring deployed models and performance tracking.

Lesson 9: Ethics and Future of Machine Learning 3 Topics

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Ethical considerations in deploying machine learning models.

The impact of AI and machine learning on society.

Future trends in machine learning and Azure ML developments.

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Training sessions have been delivered at Microsoft, Bosch, Deloitte, Michelin, IBM, Collins Aerospace, and Cognizant. We are in no way associated with or sponsored by these companies.

This educational material is independently created and offered by Bot Campus AI. It is not sponsored by,endorsed by, or affiliated with Microsoft Corporation, Amazon Technologies, Inc., Google LLC, the Python Software Foundation, or Tableau Software, LLC. The use of these terms in our course material is for descriptive purposes only and does not imply any affiliation with or endorsement by the trademark owners.