You will work with structured datasets provided within the course to build and evaluate regression and classification models using Orange.

You will gain skills in building no-code machine learning workflows, applying regression and classification algorithms, evaluating model performance, and designing end-to-end predictive analytics solutions using Orange.

Yes. The course starts with foundational concepts and gradually progresses to advanced modeling techniques, making it suitable for beginners.

Your learning is evaluated through module quizzes, hands-on assessments, and a final assessment that tests your ability to build and evaluate complete machine learning workflows.

The course is self-paced and can typically be completed within a few weeks, depending on your learning pace and time commitment.

Orange is a visual machine learning and data mining platform. It lets you build complete ML workflows by connecting components on a canvas — no programming required. It's widely used in academia, research, and business analytics.

All ML techniques — from building regression workflows to tuning classification models — are taught through detailed instructor video demonstrations. You're expected to install Orange on your own machine, follow along with the instructor in real time, and pause the video whenever you need to replicate a step or revisit a concept before moving on.

Regression predicts continuous numerical outcomes such as sales figures or temperatures, while classification predicts categorical outcomes such as fraud or no fraud. Both model types are taught in full, with separate modules and hands-on demonstrations for each.

You'll use RMSE, MAE, and R² for regression models, and confusion matrices, precision, recall, F1-score, and ROC curves for classification models — all visualized and interpreted directly within Orange's workflow canvas.

Yes. You'll learn how to systematically adjust model parameters in Orange to improve accuracy, reduce overfitting, and select the best-performing model. The tuning process is fully demonstrated so you can follow along and experiment with your own settings.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.