Book Image

MATLAB for Machine Learning

By : Giuseppe Ciaburro, Pavan Kumar Kolluru
Book Image

MATLAB for Machine Learning

By: Giuseppe Ciaburro, Pavan Kumar Kolluru

Overview of this book

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.

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MATLAB for Machine Learning
Giuseppe Ciaburro | Pavan Kumar Kolluru
Aug, 2017 | 382 pages
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Table of Contents (17 chapters)
Title Page
Title Page
Credits
Credits
Foreword
Foreword
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Getting Started with MATLAB Machine Learning
Getting Started with MATLAB Machine Learning
ABC of machine learning
Discover the different types of machine learning
Choosing the right algorithm
How to build machine learning models step by step
Introducing machine learning with MATLAB
Statistics and Machine Learning Toolbox
Neural Network Toolbox
Statistics and algebra in MATLAB
Summary
2
Importing and Organizing Data in MATLAB
Importing and Organizing Data in MATLAB
Familiarizing yourself with the MATLAB desktop
Importing data into MATLAB
Exporting data from MATLAB
Working with media files
Data organization
Summary
3
From Data to Knowledge Discovery
From Data to Knowledge Discovery
Distinguishing the types of variables
Data preparation
Exploratory statistics - numerical measures
Exploratory visualization
Summary
4
Finding Relationships between Variables - Regression Techniques
Finding Relationships between Variables - Regression Techniques
Searching linear relationships
How to create a linear regression model
Polynomial regression
Regression Learner App
Summary
5
Pattern Recognition through Classification Algorithms
Pattern Recognition through Classification Algorithms
Predicting a response by decision trees
Probabilistic classification algorithms - Naive Bayes
Describing differences by discriminant analysis
Find similarities using nearest neighbor classifiers
Classification Learner app
Summary
6
Identifying Groups of Data Using Clustering Methods
Identifying Groups of Data Using Clustering Methods
Introduction to clustering
Hierarchical clustering
Partitioning-based clustering methods - K-means algorithm
Partitioning around the actual center - K-medoids clustering
Clustering using Gaussian mixture models
Summary
7
Simulation of Human Thinking - Artificial Neural Networks
Simulation of Human Thinking - Artificial Neural Networks
Getting started with neural networks
Basic elements of a neural network
Neural Network Toolbox
A neural network getting started GUI
Data fitting with neural networks
Summary
8
Improving the Performance of the Machine Learning Model - Dimensionality Reduction
Improving the Performance of the Machine Learning Model - Dimensionality Reduction
Feature selection
Feature extraction
Summary
9
Machine Learning in Practice
Machine Learning in Practice
Data fitting for predicting the quality of concrete
Classifying thyroid disease with a neural network
Identifying student groups using fuzzy clustering
Summary

Partitioning-based clustering methods - K-means algorithm

K-means clustering is a partitioning method and as anticipated, this method decomposes a dataset into a set of disjoint clusters. Given a dataset, a partitioning method constructs several partitions of this data, with each partition representing a cluster. These methods relocate instances by moving them from one cluster to another, starting from an initial partitioning.

The K-means algorithm

The K-means algorithm is a clustering algorithm designed in 1967 by MacQueen which allows the dividing of groups of objects into K partitions based on their attributes. It is a variation of the expectation-maximization (EM) algorithm, whose goal is to determine the K data groups generated by Gaussian distributions. The K-means algorithm differs in the method used for calculating the Euclidean distance while calculating the distance between each of two data items; EM uses statistical methods.

In K-means, it is assumed that object attributes can be...

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