• Online, Self-Paced
Course Description

Classification models are used in the real world to predict whether to buy, sell, or hold a particular stock or to identify objects in images. RapidMiner studio supports features such as Turbo Prep and Auto Model that completely automate data processing and model building.

In this course, discover how classification models can be used to categorize input records and how metrics such as accuracy, precision, and recall can be used to evaluate those classification models. Next, create a process to retrieve, summarize, and visualize data using operators. Finally, configure your own workflow for classification, and train and compare a logistic regression model and a random forest model. You will choose the best-performing model for local deployment on your machine and see how you can use deployed models for predictions.

Once you have completed this course you will have the skills to train, clean, and process data in order to train classification models and deploy your model locally.

Learning Objectives

{"discover the key concepts covered in this course"}

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Systems Architecture