Description

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.