The goal of this course is to get people with a primary job function outside the field of statistics and analytics started with Machine Learning (ML).
In this two-day course you will first get familiar with the basic concepts of Machine Learning. Subsequently, you will learn about
several no-code Microsoft Azure tools that can be used to create and deploy ML models without the need for extensive Machine Learning knowledge.
This training starts from data that has already been prepared and uploaded to Azure. If you are interested in
tackling this as well, consider attending our 5 day course Data Engineering and AI on the Microsoft Azure Platform.
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 wish 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.
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
- What is Machine Learning?
- What questions can Machine Learning answer?
- Machine Learning Methodology
- Data preparation
- Data Modeling
- Model evaluation
Tools for Machine Learning in Azure
In this introductory chapter we will introduce the different tools that are available for (citizen) data scientists to do Machine Learning in Microsoft Azure.
- Overview of Machine Learning in Azure
- Machine Learning with pretrained models
- Using Transfer Learning
- Graphical Approaches to Machine Learning
- Machine Learning using Coding Approaches
Azure 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. Some of these are ready-made, whearas others are customizable.
- Overview of Cognitive Services
- Pre-trained Services
- Customizable Services
- Getting Started with LUIS
Azure Machine Learning: Automated ML
Azure Machine Learning is a service that helps to bring Machine Learning to the enterprise level. 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
- Architecture of Azure Machine Learning
- Important concepts in Azure Machine Learning
- What is Automated Machine Learning?
- Building Automated ML models
- Deploying and consuming Automated ML Models
Azure Machine Learning Service: Designer
A second service available in Azure Machine Learning 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 perform on your data,
such as a data transformation, training an algorithm, scoring new data, and validating a model.
- What is the Designer?
- Modules for Loading data
- Preprocessing data
- Training Machine Learning Models
- Testing 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 deployed Azure Machine Learning models can be consumed in Power BI Dataflows and Power Query.
- Introduction to Power BI
- Using Cognitive Services or Deployed Azure ML models in Power Query
- Using Cognitive Services or Deployed Azure ML models in Power BI Dataflows
- More Machine Learning options in Power BI