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

Understanding DL basic concepts

DL is a branch of ML based on using algorithms to model high-level abstractions about data. This discipline is part of a range of approaches that aim to learn methods for representing data. For example, an observation such as an image can be described in different ways: as a vector of intensity values for each pixel, or more abstractly as a set of edges or regions that have shapes or significant features. Some of these possible representations may prove more effective than others in facilitating the process of training another ML system.

For automatically identifying and extracting relevant features from raw data, we can use automated feature extraction to eliminate the need for manual feature engineering (FE). This process streamlines ML tasks and improves model performance.

Automated feature extraction

In this context, one of the central aspects of DL is the development of learning algorithms that specialize in automatically extracting significant...