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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.
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.
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.
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.
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.
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.
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: Machine Learning is widely applicable. As the interest for machine learning is growing, the audience that wants to create such models is 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 cognitive 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 Models 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 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.