Overview

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.

Syllabus

Introduction to Orange, ML Foundations and Mathematics

Build a strong foundation in no-code data science by learning how to use Orange for visual data mining while developing core machine learning and mathematical concepts. Explore the Orange interface, widgets and workflow design, then strengthen your understanding of linear algebra, probability and optimization fundamentals. Gain conceptual clarity on machine learning types, model evaluation strategies and common pitfalls like overfitting, preparing you for practical modeling workflows in later modules.

Regression Modeling - Basic to Advanced

Develop practical regression modeling skills by progressing from linear regression fundamentals to advanced algorithms such as Support Vector Machines and Random Forests. Learn how to select features, build and compare regression models in Orange and evaluate performance using industry-standard metrics like RMSE, MAE and R². Strengthen your ability to optimize models through hyperparameter tuning and residual analysis to produce accurate, reliable predictions.

Classification Modeling - Basic to Advanced

Master classification techniques by building, evaluating and tuning models for categorical prediction problems. Start with core classification concepts and algorithms such as logistic regression, decision trees, KNN and Naive Bayes, then advance to SVM and Random Forest classifiers. Learn to interpret confusion matrices, ROC curves and performance metrics while applying hyperparameter tuning to select the best-performing models for real-world classification tasks.

Course Wrap-Up

Consolidate your learning by revisiting the complete no-code data science workflow, from data exploration and mathematical foundations to regression and classification modeling. Reinforce key concepts, modeling decisions, and evaluation techniques while demonstrating your ability to build end-to-end machine learning solutions using Orange through a final assessment.