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

Part 1: Getting Started with Matlab

This part provides background information and essential knowledge about MATLAB tools, along with an introduction to basic machine learning concepts. We also will focus on importing and organizing data in MATLAB, emphasizing familiarity with the MATLAB workspace for simplicity in operations. The discussion covers analyzing various data formats, moving data in and out of MATLAB, exploring datatypes for grouping variables and categorical data, exporting data in different formats such as cell arrays, structure arrays, and tabular data, and saving it in MATLAB-supported file formats. The ultimate goal is to prepare data in the right format for the subsequent phase of data analysis.

This part has the following chapters: