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MATLAB for Machine Learning

You're reading from MATLAB for Machine Learning

Product type Book

Published in Aug 2017

Publisher Packt

ISBN-13 9781788398435

Pages 382 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Authors (2):

Giuseppe Ciaburro

Pavan Kumar Kolluru

View More author details

Table of Contents (17) Chapters

Title Page

Credits

Foreword

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Getting Started with MATLAB Machine Learning

2. Importing and Organizing Data in MATLAB

3. From Data to Knowledge Discovery

4. Finding Relationships between Variables - Regression Techniques

5. Pattern Recognition through Classification Algorithms

6. Identifying Groups of Data Using Clustering Methods

7. Simulation of Human Thinking - Artificial Neural Networks

8. Improving the Performance of the Machine Learning Model - Dimensionality Reduction

9. Machine Learning in Practice

Regression Learner App

Previously, we described some MATLAB apps. They make fast and easy what is long and laborious. Some procedures that require the use of many functions are automated by a user-friendly environment. Additionally, we do not have to remember the names of all the functions useful to perform a specific analysis, as the graphical interface provides us with all the features available.

The Regression Learner App leads us into a step-by-step regression analysis. Through this app, import and explore data, select features, specify validation schemes, train models, and evaluate results, will be extremely simple and fast.

We can run automated training to look for the best regression model type, including linear regression models, regression trees, Gauss process regression models, vector support vehicles, and regression tree complexes. To reuse the model with new data or to perform a programmatic regression, we can export the model to the workspace or generate the MATLAB code to recreate...

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Authors (2)

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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Pavan Kumar Kolluru

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