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.
- 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 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.
- 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 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
- 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 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
- 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 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
- LAB: Using machine learning in Power BI