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 classification methods using MATLAB

Classification methods are an essential component of machine learning and data analysis. These methods allow us to categorize data into predefined classes or groups based on specific characteristics or attributes. By utilizing classification algorithms, we can train models to make predictions or assign labels to new, unseen data points. Classification plays a vital role in various domains, including image recognition, spam filtering, sentiment analysis, fraud detection, and medical diagnosis. It enables us to make informed decisions, identify patterns, and gain insights from data.

There are numerous classification algorithms available, each with its own strengths, assumptions, and applications. Some common classification methods include decision trees, support vector machines (SVMs), random forests, logistic regression, and naive Bayes classifiers. SVM has two variations: SVC for classification and SVR for regression. To effectively...