This course is designed to build on the Model basic concepts, and introduce more advanced options through a series of practical examples.
When developing a data model you may have the requirement to run more than one data flow, or link them together, set conditions or trigger an event. A Master flow can contain advanced pipeline and flow logic, multiple data flows, multiple data models, and interactions with various other functions. The Model application contains a library of machine learning algorithms, supporting functions required to for developing data science models, include Python and R scripting.
This training is based on Pyramid 2020.27. In order to recreate the examples demonstrated, make sure to download and install the example data located here.
Please adjust your screen zoom setting when you open a walkthrough to see it in full screen mode.
In this course, you will learn:
This course contains a series of examples used to explain the Master Flows and the advanced options, and how to apply and use machine learning algorithms and supporting techniques. This includes examples of supervised machine learning which is generally used to classify data or make predictions, and unsupervised learning which is used to understand relationships within datasets.