This course provides a practical introduction to machine learning techniques for data analysis in MATLAB, focusing on widely used methods for real-world technical applications. You will begin by exploring the core concepts behind machine learning, including model workflows, data preparation, and the factors that affect model performance. The course then focuses on two popular techniques—support vector machines and artificial neural networks—as well as MATLAB apps that make model building and evaluation more accessible. Using practical examples, you will prepare data, build machine learning workflows, and apply classification and regression methods to science and engineering problems. By the end of the course, you will be able to use MATLAB to develop, test, and evaluate predictive models for real-world applications. In partnership with MathWorks, enrolled learners receive access to MATLAB for the duration of the course.
What you'll learn
understand core concepts of machine learning
prepare and process data for analysis
build and evaluate models using MATLAB
apply support vector machines and artificial neural networks
Course objectives
develop predictive models for real-world applications
gain familiarity with data preparation techniques
learn how to evaluate model performance effectively