No-code ML with RapidMiner: Building & Using Classification Models

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.