MATLAB for Machine Learning, Second Edition

MATLAB as a Machine Learning Workbench 💻

Machine learning projects rarely fail because of one missing algorithm. More often, the hard work is in preparing data, choosing a sensible model, checking results, and understanding what the output actually means. MATLAB for Machine Learning, Second Edition approaches that whole workflow through MATLAB, using its toolboxes and interactive environment as a single place to explore data, train models, and evaluate performance.

Giuseppe Ciaburro’s guide is organized for readers who want a practical, MATLAB-centered path into machine learning. It begins with core concepts and the MATLAB ecosystem, then moves into the algorithms and techniques that turn raw data into working models.

From Data Import to Model Evaluation 📊

The second edition opens with the practical foundation: importing and exporting data, working with ASCII-delimited files, handling images and audio, cleaning and normalizing datasets, and using exploratory statistics and visualization to understand what you have. That groundwork matters because machine learning results depend heavily on what happens before the model is trained.

From there, the book moves through classification and regression, including decision trees, support vector machines, linear regression, categorical predictors, outlier handling, and model evaluation with techniques such as k-fold cross-validation and leave-one-out cross-validation.

Algorithms and Techniques Covered

• Clustering methods, including k-means, k-medoids, similarity measures, centroids, and hierarchical clustering
• Dimensionality reduction through feature selection, feature extraction, stepwise regression, and PCA
• Artificial neural networks, including perceptrons, activation functions, architectures, training and testing, SGD, Adam optimization, and second-order methods
• Deep learning foundations and convolutional neural networks, with coverage of convolutional layers, pooling layers, ReLUs, and fully connected layers

MATLAB toolboxes feature throughout the discussion, including the Statistics and Machine Learning Toolbox, Deep Learning Toolbox, Reinforcement Learning Toolbox, Computer Vision Toolbox, and Text Analytics Toolbox.

Who This Edition Suits 🧠

This is not a theory-first textbook that leaves implementation to the imagination. It is written for readers who want to see how machine learning methods are expressed in MATLAB and how the pieces fit together in practice. Students, researchers, engineers, and data analysts with some programming experience will find the strongest fit. Beginners can follow the early material, but the book does assume a willingness to work with code, data, and technical concepts.

The 2024 second edition updates a title first published in 2017, keeping the focus on MATLAB as a productive environment for machine learning work. If you already use MATLAB and want to extend it into classification, clustering, neural networks, and deep learning, this guide gives you a structured route through the subject.