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Machine Learning for the Citizen Data Scientist

2days
Training code
UACIT
Book this course

Overview of tools for citizen data scientists in Azure

In this introductory chapter we will start by illustrating what Machine Learning can do for a business, and how the cloud can be an ideal solution for Machine Learning. After that, we will shortly go over the different tools that are available for citizen data scientists to do Machine Learning in Microsoft Azure.

  • What is Machine Learning
  • Why Machine Learning in the cloud?
  • Machine Learning in Microsoft Azure

Introduction to Machine Learning

This classroom training does not require people to be familiar with Machine Learning. This introductory module makes sure all participants have a common ground for diving into the rest of the training by discussing the basic concepts of Machine Learning.

  • Which questions can Machine Learning answer?
  • Machine Learning methodology
  • Data preparation
  • Classes of Machine Learning algorithms
  • Model evaluation

Cognitive Services

Business Intelligence for many years focused on turning data stored in structured, relational databases into insights or actionable information. There is however plenty of useful data that less easy to access such as plain text, images, phone recordings, ... . Cognitive services provides web services hosted in Microsoft Azure to convert these sources into an easier to analyze format (mostly json documents). In this chapter we will give an overview of the different cognitive services, where we will introduce the vision, speech, language, web search, and decision APIs. Some of these services are ready-made, whearas others are customizable.

  • Overview of cognitive services
  • Ready-made services
  • Customizable services

Azure Machine Learning Service: Automated ML

Azure Machine Learning Service is a service that helps to bring Machine Learning to the enterprise level, for example by offering tools that help with documentation, deployment, high availability and performance. This service contains tools for data scientists, as well as data citizens. One of the tools that may be especially useful for citizen data scientists is Automated ML, where Machine Learning is done in an automated way, with little time investment, programming skills or domain knowledge needed.

  • Introduction to Azure Machine Learning Service
  • What is Automated Machine Learning?
  • Configuring an Automated ML run
  • Deploying and consuming an Automated ML model

Azure Machine Learning Service: Designer

A second service available in Azure Machine Learning Service is the Designer. This allows you to visually connect modules to create Machine Learning pipelines using a drag-n-drop approach. A module is an algorithm that you can perform on your data, such as a data transformation, training an algorithm, scoring new data, and validating a model.

  • What is the Designer?
  • Loading data
  • Preprocessing data
  • Creating Machine Learning Models
  • Deploying models

AI features in Power BI

Power BI is a very popular tool for visualizing data. Lately, more and more features have been added, that allow for some more advanced data analysis. Amongst others the Cognitive services and machine learning models created in the cloud can be consumed in Power BI Data Flows and Power Query.

  • Introduction to Power BI
  • Using ML models in Power BI Data Flows
  • More machine learning options in Power BI

In this two-day course we will introduce the basic concepts of Machine Learning for citizen data science. We will walk through a number of tools that can be used to create and deploy ML models in Microsoft Azure without a lot of Machine Learning or coding knowledge. We will see Azure Machine Learning Service, in which you can either let your models be created automatically (Automated Machine Learning), or where you can create your ML pipelines using a drag-n-drop approach (Designer). We will have a look at different Cognitive Services, which are AI services and cognitive APIs that you can easily use to built intelligent apps. Finally, we will see how to consume these models in Power BI.

This course is intended for people who plan on using more advanced data analysis techniques. This can be BI developers as well as data analysts. Also project managers who which to get a better overview of Machine Learning possibilities in Azure can benefit from this course. Students should have a general background in working with data, and some experience with business intelligence in general.

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