Microsoft SQL Server Analysis Services makes it easy to create data mining solutions using wizards and integrated visualizations. Particularly if you are new to machine learning, the tools in Analysis Services are an easy way to design, train, and explore data mining models. The data in your models can be stored in a cube, relational database, or any other source support by Analysis Services. After creating a model, you can put it into production by accessing the model to create predictions using prediction multiple clients, including Integration Services and ASP.NET.
The tutorials described here have not been updated for SQL Server 2017. You can use the tutorials created for SQL Server 2014. Functionally, there are no changes in Data Mining features for SQL Server 2017. The steps should be identical.
Basic Data Mining Tutorial (SQL Server 2014) - This tutorial walks you through a targeted mailing scenario. It demonstrates how to use the data mining algorithms, mining model viewers, and data mining tools that are included in Analysis Services. You will build three data mining models to answer practical business questions while learning data mining concepts and tools.
Intermediate Data Mining Tutorial (SQL Server 2014) - This tutorial contains a collection of lessons that introduce more advanced data mining concepts and techniques such as, forecasting, market basket analysis, neural networks and logistic regression, and sequence clustering.
DMX Tutorials (SQL Server 2014) - The Data Mining Extensions (DMX) query language has syntax like that of SQL but can be used to create, query, and manage predictive models stored in Analysis Services. These tutorials demonstrate how to create a new mining structure and mining models by using the DMX language, and how to create DMX prediction queries for use in applications.