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MATLAB for Machine Learning
MATLAB for Machine Learning

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

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Profile Icon Kolluru Profile Icon Giuseppe Ciaburro
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Arrow left icon
Profile Icon Kolluru Profile Icon Giuseppe Ciaburro
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Paperback Aug 2017 382 pages 1st Edition
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MATLAB for Machine Learning

Importing and Organizing Data in MATLAB

Today, the amount of data generated is enormous; smartphones, credit cards, televisions, computers, home appliances, sensors, domotic systems, public and private transport, and so on are just a few examples of devices that generate data seamlessly. Such data is stored and then used for various purposes. One of these is data analysis using machine learning algorithms.

In the previous chapter, we analyzed how to build machine learning models step by step. At the start of the workflow, there is organization of data. Indeed, after collecting data, we typically need to import and preprocess. This first step is crucial for the proper functioning of the model that we will build and then for the final result.

In this chapter, we will have a look at how to import and organize our data in MATLAB. To do this, you should familiarize yourself with the...

Familiarizing yourself with the MATLAB desktop

MATLAB is an interactive working environment based on the matrix, the most natural way to express computational mathematics. Moreover, it is a programming language designed for technical computing, mathematical analysis, and system simulation. The integrated graphics make it easy to view and acquire knowledge from the data. Finally, a large library of prebuilt toolboxes allows us to immediately start with programming.

After installing, to start MATLAB, simply double-click on the icon on the desktop; or from the system prompt, type matlab. At first startup, MATLAB will show us a desk of contents with all the necessary items for its proper and smooth operation. Figure 2.1 shows the MATLAB desktop in the R2017a version:

Figure 2.1: MATLAB R2017a desktop for Windows 10

The MATLAB desktop includes the following panels:

  • Current Folder...

Importing data into MATLAB

In data analysis, a fundamental role is assumed by the exchange of such data between the analysis environment and external devices. We start from data collection and importing in MATLAB for whatever analysis we are going to do--always. As well, we will finish our activities by exporting the results. There are several ways to import and export data in the MATLAB environment depending on the different formats in which data is available. In this section, we will cover data import.

The Import Wizard

We can import files into MATLAB both interactively and programmatically. Interactively, we can import data from a disk file or the system clipboard. From a file, do any one of these three actions:

  • On the...

Exporting data from MATLAB

Many of the functions we have used to import data into MATLAB have a corresponding function that allows us to export data. At the beginning of the chapter, we learned to save our data for later use with the save command:

>> save filename.mat

Remember, this command saves all contents of the workspace in a compressed file with a .mat extension, called a MAT-file.

The dlmread() function allows us to handle text files with a specified delimiter. We can use this function to write a matrix to an ASCII-delimited file. To test the function, we start from a matrix of random numbers:

>> MyMatrix = rand(5)
MyMatrix =
0.7577 0.7060 0.8235 0.4387 0.4898
0.7431 0.0318 0.6948 0.3816 0.4456
0.3922 0.2769 0.3171 0.7655 0.6463
0.6555 0.0462 0.9502 0.7952 0.7094
0.1712 0.0971...

Working with media files

Images, videos, and audio files nowadays are an integral part of our lives. We use them widely thanks to the capabilities provided by modern devices that enable us to capture, store, and manipulate them. They often represent the input data we use in our machine learning applications, because through such algorithms, it is possible to extract a lot of information that at first glance is not so obvious. In this section, we will learn to deal with such files in the MATLAB environment.

Handling images

Raster images are made up of a grid of colored pixels. In MATLAB, images can be represented as two-dimensional matrices, where each matrix element corresponds to a single pixel of the displayed image. For...

Data organization

So far, for data organization, we have mostly used standard arrays that represent useful data structures for storing a large number of objects, but all of the same type, such as a matrix of numbers or characters. However, such arrays cannot be used if you want to memorize both numbers and strings in the same object. This is a problem that can be solved by so-called cell arrays, structure arrays, and more generally all those structures that the MATLAB programming environment provides us.

Cell array

A cell array is a datatype that has indexed data containers called cells. Each cell can contain any type of data; cell arrays can contain, for example, text strings, combinations of text and numbers, or numeric...

Summary

In this chapter, we started to explore the MATLAB desktop and how to easily interact with it. We took a look at MATLAB Toolstrip and how it is organized into a series of tabs. Then we just used MATLAB as a calculator and learned to manipulate matrices.

Next, we discovered the importing capabilities of MATLAB for reading several input types of data resources. We also learned how to import data into MATLAB interactively and programmatically. Afterwards, we understood how to export data from the workspace and working with media files.

Finally, we introduced data organization. We learned how to work with a cell array, structure array, table, and categorical array.

In the next chapter, we will learn the different datatypes in machine learning and how to clean the data and identify missing data. In addition, we will understand how to work with outliers and derived variables...

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Key benefits

  • 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.

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.

Who is this book 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.

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.
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Table of Contents

9 Chapters
Getting Started with MATLAB Machine Learning Chevron down icon Chevron up icon
Importing and Organizing Data in MATLAB Chevron down icon Chevron up icon
From Data to Knowledge Discovery Chevron down icon Chevron up icon
Finding Relationships between Variables - Regression Techniques Chevron down icon Chevron up icon
Pattern Recognition through Classification Algorithms Chevron down icon Chevron up icon
Identifying Groups of Data Using Clustering Methods Chevron down icon Chevron up icon
Simulation of Human Thinking - Artificial Neural Networks Chevron down icon Chevron up icon
Improving the Performance of the Machine Learning Model - Dimensionality Reduction Chevron down icon Chevron up icon
Machine Learning in Practice Chevron down icon Chevron up icon

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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
(6 Ratings)
5 star 16.7%
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3 star 16.7%
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1 star 16.7%
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Noel Mariano Cruz Dec 19, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
guided step by step using matlab for machine learning
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Lorenzo Dec 27, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Buon libro per approcciarsi alle funzionalità statistiche di matlab con esempi e script chiari e comprensibili anche per principianti
Amazon Verified review Amazon
Daniela M Dec 10, 2021
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
En general, el libro llegó en buenas condiciones. Sin embargo se ve un poco sucio por fuera y, si lo observo minuciosamente, los bordes no están en perfectas condiciones. Creo que podrían mejorar el empaque, para que el libro llegue en mejores condiciones a sus destinatarios.
Amazon Verified review Amazon
Alfredo Calvimontes May 06, 2021
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A good book for beginners. The quality of the figures could be better.
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Jeff May 05, 2019
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This book does not include detailed mathematical explanation. However, it explains general steps of analysis in machine learning with three Matlab examples in last chapter.
Amazon Verified review Amazon
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