Introduction to Machine Learning
This classroom training does not require people to be familiar with Machine Learning. This introductory module
all participants have a common ground for diving into the rest of the training by discussing the basic concepts
- What is machine learning?
- Why would we use Machine Learning?
- Machine Learning methodology
- Data preprocessing
- Model evaluation: measuring quality
- LAB: Machine Learning Quiz
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
can be an ideal solution for Machine Learning. After that, we will shortly go over the different tools that are
for citizen data scientists to do Machine Learning in Microsoft Azure.
- Overview Machine Learning in Azure
- Pretrained models
- Transfer learning
- Graphical approaches
- Coding approaches
- LAB: Azure tools
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
- Pretrained services
- Customizable services
- LAB: Using Cognitive 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
by offering tools that help with documentation, deployment, high availability and performance. This service
for data scientists, as well as data citizens. One of the tools that may be especially useful for citizen data
Automated ML, where Machine Learning is done in an automated way, with little time investment, programming
domain knowledge needed.
- Introduction to Azure Machine Learning Service
- Important concepts Azure ML
- Building Automated ML Models
- Deploying and consuming an Automated ML model
- LAB: Automated ML
Azure Machine Learning Service: Designer
A second service available in Azure Machine Learning Service is the Designer. This allows you to visually
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
- LAB: Azure ML Designer
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
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
- LAB: Using machine learning in Power BI
Machine Learning uses the data collected by organizations to build models which
help in predicting future events. From analyzing sales opportunities up to predicting web server activity:
is widely applicable. As the interest for machine learning is growing, the audience that wants to create such
getting more varied. Business people with an interest in machine learning are known as citizen data scientists.
In this two-day course the basic concepts of Machine Learning for citizen data science are introduced. A number
of tools are introduced that can be used to create and deploy ML models without a lot of Machine Learning or
coding knowledge in Microsoft Azure. Azure Machine Learning Service allows
your models to
be created automatically (Automated Machine Learning), or you can create your ML pipelines using a
drag-n-drop approach (Designer). Cognitive Services are shown as well. These are AI services and
APIs that you can easily use to build intelligent apps, without the need to have AI knowledge. Finally, you will
have a look
at the AI features that are available in Power BI, such as built-in AI visuals, and the possibility to use a ML
model that you created in Azure Machine Learning or Cognitive services, in Power Query.
Students will get an overview of the tools in Microsoft Azure that can help them creating Machine Learning
without a lot of ML knowledge and without the need to code. They will learn about the basic concepts
in Machine Learning.
This course is intended for people who plan on using machine learning without writing code. This can be BI
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
with business intelligence in general.