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MATLAB for Machine Learning

By : Kolluru, Giuseppe Ciaburro

3.5 (6)

Buy this Book

MATLAB for Machine Learning

3.5 (6)

By: Kolluru, Giuseppe Ciaburro

Buy this Book

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.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

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

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

From Data to Knowledge Discovery

Distinguishing the types of variables

Data preparation

Exploratory statistics - numerical measures

Exploratory visualization

Summary

Finding Relationships between Variables - Regression Techniques

Searching linear relationships

How to create a linear regression model

Polynomial regression

Regression Learner App

Summary

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

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

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

Improving the Performance of the Machine Learning Model - Dimensionality Reduction

Feature selection

Feature extraction

Summary

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

Data fitting with neural networks

Data fitting is the process of building a curve or a mathematical function that has the best match with a set of previously collected points. Curve fitting can relate to both interpolations, where exact data points are required, or smoothing, where a flat function that approximates the data is built. We are talking about curves fitting in a regression analysis, which is most concerned with statistical inference problems, as well as the uncertainty that a curve coincides with observed data that has random errors. The approximate curves obtained from the data fitting can be used to help display data, to predict the values of a function where no data is available, and to summarize the relationship between two or more variables.