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

Introducing ML

ML is based on the idea of providing computers with a large amount of input data, together with the corresponding correct answers or labels, and allowing them to learn from this data, identifying patterns, relationships, and regularities within them. Unlike traditional programming approaches, in which computers follow precise instructions to perform specific tasks, ML allows machines to independently learn from data and make decisions based on statistical models and predictions.

One of the key concepts of ML is the ability to generalize. This means that a model trained on information in the training dataset should be able to make accurate predictions about new data that it has never seen before. This allows ML to be applied across a wide range of domains.

How to define ML

To better understand the basic concepts of ML, we can start from the definitions formulated by the pioneers in this field. According to Arthur L. Samuel (1959) – “ML is a field...