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Azure Machine Learning Development: Part 2

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ویدئو معرفی این محصول

Machine learning as a concept has been around for over 60 years, but as artificial intelligence becomes more and more popular, there’s increased opportunity in the field of artificial intelligence and its subgroups, machine learning and deep learning. And with the explosion of cloud computing, it’s only natural to study machine learning with cloud technologies like Azure. In this second course to her Azure machine learning series, Zarina Meeran starts you off with a look at AI and how machine learning is a subgroup of AI—an important concept to understand before diving into the various ML problems and ML algorithms to solve these problems. She then covers the ML lifecycle to get the end-to-end picture as well as the tools offered from Azure. Finally, Zarina goes through the architecture of MLOps within the cloud as currently adopted.


01 - Introduction
  • 01 - Importance of Azure machine learning development

  • 02 - 1. Azure Machine Learning
  • 01 - What is machine learning
  • 02 - What is Azure machine learning
  • 03 - Machine learning algorithms
  • 04 - Machine learning lifecycle

  • 03 - 2. Getting Started with Azure Machine Learning
  • 01 - Creating a workspace
  • 02 - Azure Machine Learning Studio walkthrough

  • 04 - 3. Automated Machine Learning
  • 01 - What is automated ML
  • 02 - How to use automated ML

  • 05 - 4. Using the Azure ML Designer Feature
  • 01 - What is Azure Machine Learning designer
  • 02 - Train a linear regression model
  • 03 - Deploy the linear regression model

  • 06 - 5. Author a Notebook
  • 01 - Authoring a notebook with Azure ML
  • 02 - Run a Python script
  • 03 - Train your ML model

  • 07 - Conclusion
  • 01 - Next steps