Octave for Machine Learning: Analyze & Visualize

Build a strong foundation in GNU Octave for machine learning by learning how to compute, analyze, and visualize data through hands-on practice. This beginner-friendly course guides you from installing and configuring Octave to performing matrix operations, processing strings, managing data structures, applying symbolic mathematics, and creating meaningful data visualizations. You will begin by exploring Octave's interface and core numerical computing capabilities before progressing to matrix creation, subsetting, multiplication, inversion, and other essential operations. As you advance, you will work with string manipulation, text processing, logical operators, cell arrays, and data structures used for effective data handling and preprocessing. The course then introduces symbolic mathematics, including algebraic equations, limits, integrals, and polynomial computations, followed by visualization techniques such as multi-plot figures, 3D mesh grids, annotated graphs, and statistical analysis using skewness, kurtosis, and univariate analysis. Designed for beginners and aspiring machine learning learners, this course combines programming, mathematics, and visualization within Octave's open-source environment. By the end of the course, you will be able to perform numerical computations, organize and analyze data, create informative visualizations, and apply Octave's computational tools to build a strong foundation for future machine learning and data science studies.