Book Image

MATLAB for Machine Learning - Second Edition

By : Giuseppe Ciaburro
Book Image

MATLAB for Machine Learning - Second Edition

By: Giuseppe Ciaburro

Overview of this book

Discover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications. By navigating the versatile machine learning tools in the MATLAB environment, you’ll learn how to seamlessly interact with the workspace. You’ll then move on to data cleansing, data mining, and analyzing various types of data in machine learning, and visualize data values on a graph. As you progress, you’ll explore various classification and regression techniques, skillfully applying them with MATLAB functions. This book teaches you the essentials of neural networks, guiding you through data fitting, pattern recognition, and cluster analysis. You’ll also explore feature selection and extraction techniques for performance improvement through dimensionality reduction. Finally, you’ll leverage MATLAB tools for deep learning and managing convolutional neural networks. By the end of the book, you’ll be able to put it all together by applying major machine learning algorithms in real-world scenarios.
Table of Contents (17 chapters)
Free Chapter
1
Part 1: Getting Started with Matlab
4
Part 2: Understanding Machine Learning Algorithms in MATLAB
9
Part 3: Machine Learning in Practice

Building an effective and accurate classifier

Classification in machine learning is a supervised learning task that involves categorizing or classifying data into predefined classes or categories. It is one of the fundamental and widely used techniques in machine learning and data mining. The goal of classification is to develop a model or classifier that can accurately assign new, unseen instances to the correct class based on their features or attributes. The classifier learns patterns and relationships from a labeled training dataset, where each instance is associated with a known class label.

We will first discuss SVMs.

SVMs explained

SVMs are powerful supervised machine learning algorithms used for classification and regression tasks. They are particularly effective in solving complex problems with a clear margin of separation between classes. SVMs can handle both linearly separable and non-linearly separable data by transforming the input space into a higher-dimensional...