StoryQ is a web-based machine learning and text mining tool that allows young learners (Grades 6-12) to engage in machine learning practices and work with unstructured text data without coding. StoryQ features dynamically linked data representations that promote meaningful inquiries across tables, graphs, and texts––a unique user experience that makes machine learning models transparent, explainable, and fun to explore. This demo will showcase how key AI concepts such as representation, reasoning, feature space, feature weight, and machine learning are dynamically visualized in StoryQ. A brief tutorial will be provided on how to use StoryQ to train, test, and troubleshoot text classification models using both standard feature extractors (e.g., N-grams) and special feature extraction tools and visualizations designed to support young learners and non-computing teachers. This demo also includes sample learning activities designed for high school English Language Arts and History classes to showcase how machine learning concepts and practices can be introduced in non-computing classes. This work is supported by an NSF I-TEST project (DRL-1949110).