Applied Machine Learning Without Coding

Course Objectives:

This course aims to provide students from all academic backgrounds with a practical, hands-on introduction to the fundamental concepts and applications of Machine Learning (ML) using the KNIME Analytics Platform. By the end of the course, students will be able to describe and navigate the typical ML workflow, effectively use KNIME for data handling tasks (loading, exploration, visualization, preprocessing), explain the principles behind common algorithms (like Decision Trees, K-Nearest Neighbors, Linear Regression, K-Means), differentiate between supervised and unsupervised learning, and build, train, evaluate, and interpret simple ML models within the context of a given problem, fostering critical thinking and problem-solving skills.

Course Content:

The “Applied Machine Learning Without Coding” course introduces fundamental ML concepts and the standard ML pipeline (workflow). It covers techniques for data exploration through visualization and essential data preprocessing steps, including cleaning, preparation, and feature handling using the KNIME platform. The curriculum delves into core ML tasks, explaining classification, regression, and clustering. Emphasis is placed on evaluating model performance using appropriate metrics. The course structure incorporates hands-on workshops using KNIME to reinforce these concepts and culminates in a group project where students apply learned techniques to a real-world dataset, from planning and data selection to model building and presenting results.