No-Code Machine Learning Classification with Orange

Predicting outcomes and categorizing data are fundamental tasks in modern data science, but you do not need complex programming skills to get started. This written course guides you through machine learning classification workflows using the open-source Orange data mining tool. You will start with core terminology and fundamental classification principles before progressing to practical workflows. Through clear text explanations and step-by-step written exercises, you will discover how to import datasets, handle missing values, configure predictive algorithms, and assess performance metrics. What you'll learn: Understand key machine learning and classification concepts from the ground up; Prepare, clean, and preprocess tabular datasets for modeling; Configure popular classification algorithms including decision trees, logistic regression, and random forests; Evaluate model performance using confusion matrices, ROC curves, precision, and recall; Apply cross-validation techniques to prevent overfitting and ensure reliable predictions; Explore modern concepts in model interpretability and feature importance. The material progresses systematically from foundational concepts and data preprocessing to model training and comparative evaluation. This course is designed for beginners, business analysts, and aspiring data practitioners who want to perform machine learning analysis without writing code. Start reading today to build practical no-code predictive modeling skills.