This hands-on pathway builds practical machine learning capability using GNU Octave—the open-source MATLAB alternative—plus a focused module in R for classification. Across four Octave courses you’ll progress from installation and core matrix operations to data wrangling, visualization (2D/3D, mesh, annotated plots), control structures, reusable functions, and time-series handling. You’ll then apply supervised learning with logistic regression in R, covering preprocessing, evaluation (confusion matrix, ROC/AUC), and threshold decisions. Graduates leave ready to prototype ML workflows and analyze real datasets efficiently for data science and analytics roles.

Applied Learning Project

Projects mirror industry scenarios: clean and explore CSV datasets in Octave, compute descriptive statistics, and build visual dashboards; write reusable Octave functions to automate time-series feature extraction; and complete two capstone mini-projects—credit-risk scoring and diabetes prediction—using logistic regression in R, including threshold tuning and ROC analysis to justify model choices to stakeholders.