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MATLAB for Machine Learning

You're reading from MATLAB for Machine Learning Practical examples of regression, clustering and neural networks

Product type Paperback

Published in Aug 2017

Publisher Packt

ISBN-13 9781788398435

Length 382 pages

Edition 1st Edition

Languages

MATLAB

Tools

MATLAB

Concepts

Machine Learning

Authors (2):

Pavan Kumar Kolluru

Giuseppe Ciaburro

View More author details

Table of Contents (10) Chapters

Preface

1. Getting Started with MATLAB Machine Learning FREE CHAPTER

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

Exploratory statistics - numerical measures

In the exploratory phase of a study, we try to gather a first set of information needed to derive features that can guide us in choosing the right tools to extract knowledge from the data. Those analyses provide a variety of tools for quickly summarizing and gaining insight about a set of data. The purpose of exploratory analysis is to use statistical indicator and visualizations to better understand the data, find clues about data trends and its quality, and formulate hypotheses from our analysis. We do not want to make imaginative or aesthetically pleasing views to surprise the interlocutor; our goal is to try to answer specific questions through data analysis.

Measures of location

...

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Authors (2)

Kolluru

Kolluru

See other products by Kolluru

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).

See other products by Giuseppe Ciaburro

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