Azure Machine Learning eBook

Description

Microsoft Azure Essentials: Azure Machine Learning” by Jeff Barnes is part of the Microsoft Azure Essentials series, which provides a practical introduction to various Azure services. This book specifically focuses on Azure Machine Learning (Azure ML), offering a comprehensive guide to understanding and using this powerful tool for building and deploying machine learning models in the cloud.

Key Topics Covered in the Book:

1. Introduction to Machine Learning and Azure ML:
   • Basic concepts of machine learning.
   • Overview of Azure Machine Learning service and its benefits.
   • Use cases for machine learning in the cloud.
2. Getting Started with Azure ML:
   • Setting up an Azure Machine Learning workspace.
   • Navigating the Azure ML Studio interface.
   • Creating and managing datasets.
3. Data Preparation:
   • Importing and cleaning data.
   • Feature engineering and selection.
   • Data transformation techniques.
4. Building Machine Learning Models:
   • Overview of different types of machine learning models (regression, classification, clustering, etc.).
   • Selecting algorithms for model training.
   • Training and evaluating models.
5. Model Deployment:
   • Deploying machine learning models as web services.
   • Creating real-time endpoints for model inference.
   • Monitoring and managing deployed models.
6. Automated Machine Learning (AutoML):
   • Using Azure ML’s AutoML capabilities to automatically train and tune models.
   • Understanding the workflow and benefits of AutoML.
7. Advanced Topics:
   • Deep learning and neural networks in Azure ML.
   • Integrating with other Azure services (e.g., Azure Data Factory, Azure Databricks).
   • Using Jupyter Notebooks in Azure ML.
8. Case Studies and Practical Applications:
   • Real-world examples of machine learning solutions built with Azure ML.
   • Best practices for developing and deploying machine learning models.

Benefits of Using Azure Machine Learning:

• Scalability: Easily scale machine learning experiments and deployments.
• Collaboration: Collaborate with team members through a unified platform.
• Productivity: Increase productivity with automated machine learning and pre-built tools.
• Integration: Seamlessly integrate with other Azure services for comprehensive data solutions.

Practical Applications:

• Predictive maintenance in manufacturing.
• Customer churn analysis in marketing.
• Fraud detection in finance.
• Image and text analysis in various industries.

Target Audience:

This book is aimed at data scientists, machine learning engineers, and developers who want to leverage Azure Machine Learning to build, train, and deploy machine learning models. It provides hands-on examples, step-by-step tutorials, and best practices to help readers gain practical experience and insights into machine learning in the Azure ecosystem.

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