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.
Title Page
Credits
Foreword
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Preface
Free Chapter
Getting Started with MATLAB Machine Learning
Importing and Organizing Data in MATLAB
From Data to Knowledge Discovery
Finding Relationships between Variables - Regression Techniques
Pattern Recognition through Classification Algorithms
Identifying Groups of Data Using Clustering Methods
Simulation of Human Thinking - Artificial Neural Networks
Improving the Performance of the Machine Learning Model - Dimensionality Reduction
Machine Learning in Practice

Summary

This final chapter has served us to revise the concepts learned in previous chapters; this time, without any introductions, but starting with a real-life case and analyzing the workflow that allows us to extract knowledge from a database.

In this chapter, we started with solving a fitting problem. We created a model that allows us to calculate the concrete compressive strength according to the ingredients used in the mixture. We learned how to import data in the MATLAB workspace and how to prepare it for subsequent analysis. Then, we resolved a fitting problem using Neural Network Toolbox.

Then, we learned how to use neural network to classify pattern. In this study, we created a model that allows us to classify thyroid diseases according to a lot of patient data. This time, we used a dataset that was already available in the MATLAB distribution. We also learned to build and understand the confusion matrices and the ROC.

Finally, we performed a clustering analysis. The purpose of this...