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 anomaly detection and fault diagnosis systems

Anomaly detection and fault diagnosis systems are crucial components of various industries, particularly in areas where safety, reliability, and efficiency are of utmost importance, such as manufacturing, healthcare, finance, and cybersecurity. These systems aim to identify unusual or unexpected patterns, behaviors, or conditions in data, processes, or systems that may indicate the presence of faults, defects, or anomalies.

Delving into the realm of anomaly detection, this section provides a comprehensive overview, unraveling the key principles and methodologies employed in identifying deviations from the norm within diverse systems and datasets.

Anomaly detection overview

Anomaly detection is a technique used in data analysis and ML to identify data points or patterns that deviate significantly from the expected or normal behavior within a dataset. Anomalies, also known as outliers, are data points that do not conform...