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MATLAB for Machine Learning

 

MATLAB for Machine Learning

Autor: Giuseppe Ciaburro
Broj strana: 382
ISBN broj: 9781788398435
Izdavač: PACKT PUBLISHING
Godina izdanja: .

Pregleda (30 dana / ukupno): 35 / 142

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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.

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.

Authors

Giuseppe Ciaburro

Giuseppe Ciaburro holds a master's degree in chemical engineering from Università degli Studi di Napoli Federico II, and a master's degree in acoustic and noise control from Seconda Università degli Studi di Napoli. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli".

He has over 15 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 Python and R, and he has extensive experience of working with MATLAB. An expert in acoustics and noise control, Giuseppe has wide experience in teaching professional computer courses (about 15 years), dealing with e-learning as an author. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He is currently researching machine learning applications in acoustics and noise control.

Table of Contents

Chapter 1: Getting Started with MATLAB Machine Learning
Chapter 2: Importing and organizing data in Matlab
Chapter 3: From data to knowledge discovery
Chapter 4: Finding relationships between variables - Regression techniques
Chapter 5: Pattern recognition through classification algorithms
Chapter 6: Identifying groups of data by clustering methods
Chapter 7: Simulation of human thinking - Artificial neural networks
Chapter 8: Improves the performance of the machine learning model - Dimensionality reduction
Chapter 9: Machine learning in practice
Chapter 10: Feedback

 

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