Practical Machine Learning for Engineers in MATLAB

Engineering and technical fields generate massive amounts of data, but extracting actionable insights requires the right analytical tools. This course introduces you to the essentials of machine learning, showing you how to turn complex datasets into predictive models. You will progress from understanding core machine learning terminology to implementing practical workflows in MATLAB. By working through clear, written explanations and structured code examples, you will gain the confidence to prepare engineering data, train robust models, and evaluate their performance. What you'll learn: - Understand foundational machine learning concepts, terminology, and standard model workflows. - Prepare and preprocess engineering datasets, handling missing values and performing feature selection. - Train support vector machines (SVMs) and artificial neural networks using MATLAB. - Evaluate model performance using modern metrics, cross-validation techniques, and confusion matrices. - Apply machine learning techniques to solve real-world technical and engineering problems. The course begins with foundational definitions and data preparation techniques before moving into supervised learning algorithms and model optimization. You will read through step-by-step code implementations and practical scenarios designed for technical professionals. This course is designed for engineers, researchers, and technical analysts who are new to machine learning and want to apply it using MATLAB. No prior machine learning experience is required, though a basic familiarity with MATLAB is helpful. Start reading today to unlock the power of predictive modeling in your engineering projects.