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MATLAB for Machine Learning

You're reading from MATLAB for Machine Learning Unlock the power of deep learning for swift and enhanced results

Product type Paperback

Published in Jan 2024

Publisher Packt

ISBN-13 9781835087695

Length 374 pages

Edition 2nd Edition

Languages

MATLAB

Tools

MATLAB

Concepts

Deep Learning

Author (1):

Giuseppe Ciaburro

View More author details

Table of Contents (17) Chapters

Preface

1. Part 1: Getting Started with Matlab

2. Chapter 1: Exploring MATLAB for Machine Learning FREE CHAPTER

3. Chapter 2: Working with Data in MATLAB

4. Part 2: Understanding Machine Learning Algorithms in MATLAB

5. Chapter 3: Prediction Using Classification and Regression

6. Chapter 4: Clustering Analysis and Dimensionality Reduction

7. Chapter 5: Introducing Artificial Neural Network Modeling

8. Chapter 6: Deep Learning and Convolutional Neural Networks

9. Part 3: Machine Learning in Practice

10. Chapter 7: Natural Language Processing Using MATLAB

11. Chapter 8: MATLAB for Image Processing and Computer Vision

12. Chapter 9: Time Series Analysis and Forecasting with MATLAB

13. Chapter 10: MATLAB Tools for Recommender Systems

14. Chapter 11: Anomaly Detection in MATLAB

15. Index

Why subscribe?

16. Other Books You May Enjoy

Technical requirements

In this chapter, we will introduce basic machine learning concepts. To understand these topics, a basic knowledge of algebra and mathematical modeling is needed. You will also require a working knowledge of MATLAB.

To work with the MATLAB code in this chapter, you’ll need the files available on GitHub at https://github.com/PacktPublishing/MATLAB-for-Machine-Learning-second-edition.

The rest of the chapter is locked

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Authors (1)

Giuseppe Ciaburro

Giuseppe Ciaburro

Giuseppe Ciaburro holds a PhD and two master's degrees. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli". He has over 25 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in MATLAB, Python and R. As an expert in AI applications to acoustics and noise control problems, Giuseppe has wide experience in researching and teaching. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He was recently included in the world's top 2% scientists list by Stanford University (2022).

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