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

Clustering Analysis and Dimensionality Reduction

Clustering techniques aim to uncover concealed patterns or groupings within a dataset. These algorithms detect groupings without relying on any predefined labels. Instead, they select clusters based on the similarity between elements. Dimensionality reduction, on the other hand, involves transforming a dataset with numerous variables into one with fewer dimensions while preserving relevant information. Feature selection methods attempt to identify a subset of the original variables, while feature extraction reduces data dimensionality by transforming it into new features. This chapter shows us how to divide data into clusters, or groupings of similar items. We’ll also learn how to select features that best represent the set of data.

In this chapter, we will cover the following main topics:

  • Understanding clustering – basic concepts and methods
  • Understanding hierarchical clustering
  • Partitioning-based clustering...