MATLAB for Machine Learning: Practical examples of regression, clustering and neural networks - Couverture souple

Ciaburro, Giuseppe

Couverture souple

ISBN 10 : 1788398432 ISBN 13 : 9781788398435

Editeur : Packt Publishing, 2017

Synopsis

Extract patterns and knowledge from your data in easy way using MATLAB

Key Features

• Get your first steps into machine learning with the help of this easy-to-follow guide
• Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB
• Understand how your data works and identify hidden layers in the data with the power of machine learning.

Book Description

MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners.

You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions.

You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement.

At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB.

What you will learn

• Learn the introductory concepts of machine learning.
• Discover different ways to transform data using SAS XPORT, import and export tools,
• Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data.
• Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment.
• Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures.
• Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox.
• Learn feature selection and extraction for dimensionality reduction leading to improved performance.

Who this book is for:

This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well.

A propos du livre

À propos de l'auteur

Giuseppe Ciaburro holds a PhD in environmental technical physics, along with two master's degrees. His research was focused on machine learning applications in the study of urban sound environments. He works at the Built Environment Control Laboratory at the Università degli Studi della Campania Luigi Vanvitelli, Italy. He has over 15 years' professional experience in programming (Python, R, and MATLAB), first in the field of combustion, and then in acoustics and noise control. He has several publications to his credit.

Détails de l'édition

• Éditeur: Packt Publishing
• Date d'édition: 2017
• Langue: anglais
• ISBN 10: 1788398432
• ISBN 13: 9781788398435
• Reliure: Broché
• Nombre de pages: 382
• Coordonnées du fabricant: non disponible
• Personne responsable: gpsr@libri.de; gpsr@libri.de; ; Friedensallee 273; Hamburg; 22763; Allemagne