Machine Learning and Predictive Modeling with MATLAB

Data is only as valuable as the insights you can extract from it. This text-based course guides you through the process of turning raw data into actionable predictions using MATLAB's robust computational environment. You will transition from understanding basic statistics to building, tuning, and evaluating professional machine learning models. By mastering these predictive workflows, you will be able to uncover hidden patterns in your datasets and make confident, data-backed decisions in your industry. What you'll learn: - Understand fundamental machine learning concepts, including supervised and unsupervised learning paradigms - Prepare and preprocess structured data using modern MATLAB table structures and data cleaning workflows - Train regression and classification models using MATLAB's intuitive machine learning apps and command-line tools - Evaluate model performance using key metrics such as confusion matrices, ROC curves, and cross-validation techniques - Interpret model decisions using modern feature importance and explainability methods - Deploy trained models for practical use, ensuring your predictive workflows are reproducible and scalable The journey begins with foundational machine learning definitions and data preparation techniques. You will then progress through step-by-step written explanations and practical code snippets covering model training, hyperparameter tuning, and model validation. This course is designed for beginners, domain experts, and engineers who want to leverage machine learning without needing a deep background in computer science. A basic understanding of introductory statistics is helpful, but no advanced programming experience is required. Start reading today to unlock the predictive power of your data with MATLAB.