Applied Machine Learning Without Coding

Machine learning is no longer exclusive to developers. This course gives you the hands-on skills to build, evaluate, and optimize regression and classification models using Orange Data Mining — a powerful visual ML platform — without writing a single line of code. Throughout this course, you'll move from core ML fundamentals and essential mathematics through to practical model building, evaluation, and tuning — all through an intuitive visual workflow interface designed for data professionals and business users alike. Every technique is demonstrated through clear, instructor-led video walkthroughs that you can follow along on your own Orange setup, pausing and replaying as needed to build confidence at every step. By the end of this course, you'll be able to: - Build and evaluate regression models using linear regression, SVMs, and Random Forests with visual Orange workflows. - Apply classification algorithms including logistic regression, decision trees, KNN, and Naive Bayes to solve real-world prediction problems. - Evaluate model performance using RMSE, MAE, R², confusion matrices, and ROC curves to compare and select optimal models. - Perform feature selection and hyperparameter tuning in Orange to improve model accuracy and generalization without coding. This course is designed for a diverse audience: aspiring data analysts, machine learning beginners, business analysts, domain experts, and non-technical professionals who want to explore predictive analytics through a no-code approach. Basic familiarity with data concepts and spreadsheets, is recommended before enrolling. Gain the confidence to build and interpret machine learning models that solve real business problems — all through an intuitive visual interface with Orange Data Mining

• Explain fundamental machine learning concepts, mathematical foundations, and the role of no-code tools in building analytical workflows
• Apply Orange Data Mining to build regression and classification models using visual, no-code workflows
• Analyze model performance using appropriate evaluation metrics to compare, select, and improve machine learning models
• Evaluate and optimize machine learning solutions by tuning parameters and designing end-to-end predictive workflows for real-world data

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work