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

Summary

In this chapter, we began our exploration of the MATLAB desktop and its convenient interaction features. We familiarized ourselves with the MATLAB Toolstrip, which is organized into various tabs. Subsequently, we delved into the importing capabilities of MATLAB, enabling us to read diverse types of data resources. We acquired knowledge on how to import data into MATLAB interactively and programmatically. Moreover, we comprehended the process of exporting data from the workspace and working with media files.

Next, we embarked on the challenging task of data preparation. We learned various techniques, including identifying missing values, modifying data types, replacing missing values, removing incomplete entries, organizing tables, identifying outliers, and consolidating multiple data sources. Following that, we explored exploratory statistics techniques, which enabled us to derive insightful features guiding us in selecting appropriate tools for extracting knowledge from...