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

Implementing a MATLAB model to label sentences

In this section, we will discuss a very interesting topic that is very popular in today’s society. I am referring to the importance of reviews in influencing a customer’s interest in making the right decision.

Introducing sentiment analysis

Sentiment analysis, a technique that utilizes NLP, extracts and analyzes subjective information from text. Analyzing vast datasets reveals collective opinions that impact various domains. While manual sentiment analysis is challenging, automated methods have emerged. However, automating language modeling is complex and costly due to the nuances of human language. Additionally, the methodology varies across languages, increasing complexity.

A major challenge lies in determining the polarity of opinions. Polarity classification is subjective, with one sentence perceived differently by individuals based on their value systems. The rise of social media has heightened interest in sentiment...