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Cloudswyft

Microsoft Future Ready: Designing and Implementing a Data Science Solution on Azure

Cloudswyft via FutureLearn

Overview

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This self-paced course was created by the Microsoft World Wide Learning team for data scientists, with the aim of supporting the exam DP-100: Designing and Implementing a Data Science Solution on Azure.

You’ll learn about the data science process and how solutions within Microsoft Azure can best support it. You’ll then delve into machine learning model training and deployment, allowing you to use a greater degree of automation within your data science projects.

Use the Azure machine learning to improve your data modelling

Machine learning is revolutionising a huge range of industries, including data science. Guided by industry experts, you’ll review the machine learning capabilities available in Microsoft Azure, and learn how to use it for local model training, model selection automation, model management, and monitoring.

Consolidate your knowledge by building a chatbot

Once you have built your understanding of Microsoft Azure and its functionality, you’ll put that knowledge into practice by building a simple chatbot. This offers you the opportunity to customise and troubleshoot within the platform, as well as a chance to build hands-on experience.

By the end of the course you’ll have the confidence and knowledge to design and implement a data science solution using Microsoft Azure’s AI stack.

This course is best suited to aspiring data scientists and those whose job responsibilities primarily fall into the area of data science.

Syllabus

  • Course Introduction
    • About this Course
    • Data Science
    • The Data Science Process
    • Azure and Data Science
    • Cloud-based and On-premises Machine Learning
    • Wrapping up the Week
  • Data Science on Azure
    • Development platforms and tools
    • Introduction to Data Science with Azure Notebooks
    • CloudSwyft Hands-On Lab 1
    • Introducing the Azure Machine Learning (AML) Service
    • Training Machine Learning Models
    • Wrapping Up the Week
  • Azure Machine Learning Service
    • Registering and Deploying Models
    • CloudSwyft Hands-On Lab 2
    • Automating Model Selection with AML Service
    • CloudSwyft Hands-On Lab 3
    • Managing and Monitoring Models with AML Service
    • Wrapping Up the Course

Taught by

Duncan M

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