/AI-in-cloud-workshop

AI in Microsoft Azure Workshop

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AI in Microsoft Azure Workshop

Syllabus

Introduction

Opening speech.

Requirements to the students:

  1. Modern web browser
  2. Microsoft Azure Account
  3. Basic knowledge in R or Python.

Module I: Basics of Machine Learning

Introduction to Machine Learning

Topics:

  • ML terminology, scope and relevance
  • ML tasks: supervised learning, unsupervised learning, and reinforcement learning
  • ML algorithms: regression, classification, clustering
  • Intuitive understanding of algorithms: from linear regression to neural networks.

Materials:

Advanced materials:

Azure AI Platform

Topics:

  • Data Science tools in Azure: basic programming languages, ML frameworks, and cloud services.
  • Selecting an ML service for a specific task.

Materials:

Advanced materials:

Azure Machine Learning

Topics:

  • Overview of the Azure Machine Learning
  • Azure ML Datasets: upload, getting and transforming data
  • Azure ML Designer: supported ML algorithms, train and evaluation model
  • Azure ML Notebooks: an interactive application for analyzing and developing ML models
  • AutoML in Azure ML
  • Tools Azure ML: VS Code and Azure Machine Learning Extension for interaction with Azure ML.

Materials:

Advanced materials:

Practice
  1. Create Azure ML Workspace
  2. Pima Indians Diabetes Lab in Azure ML.

Module II: Basics of Deep Learning

Introduction to Deep Learning

Topics:

  • The current stage of neural network development
  • Types of neural networks:
    • Fully connected neural networks (FNN)
    • Convolutional neural networks (CNN)
    • Recurrent neural networks (RNN)
    • Generative adversarial network (GAN).

Materials:

Advanced materials:

Deep Learning in Azure AI Platform
Azure Data Science VM

Topics:

  • Virtual machines for Data Science
  • Data Science VM images types.

Materials:

Advanced materials:

Practice
  1. Deploy of Azure Deep Learning VM on Ubuntu Server
  2. MNIST Digits Recognition Lab
  3. Bitcoin price prediction.

Module III. Large Scalable Machine Learning

Big Data Concepts

Slides (will add link asap)

Big Data Ecosystem in Azure

Practice

Azure HDInsight

Practice

Azure Databricks

Practice

Module IV. Optional Topics

Auto ML

Materials:

Practice:

MLOps

Final Project

Open Data Hubs
Research Papers Hubs
Communities