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MATLAB for Machine Learning

You're reading from   MATLAB for Machine Learning Practical examples of regression, clustering and neural networks

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781788398435
Length 382 pages
Edition 1st Edition
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Authors (2):
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Pavan Kumar Kolluru Pavan Kumar Kolluru
Author Profile Icon Pavan Kumar Kolluru
Pavan Kumar Kolluru
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Toc

Table of Contents (10) Chapters Close

Preface 1. Getting Started with MATLAB Machine Learning FREE CHAPTER 2. Importing and Organizing Data in MATLAB 3. From Data to Knowledge Discovery 4. Finding Relationships between Variables - Regression Techniques 5. Pattern Recognition through Classification Algorithms 6. Identifying Groups of Data Using Clustering Methods 7. Simulation of Human Thinking - Artificial Neural Networks 8. Improving the Performance of the Machine Learning Model - Dimensionality Reduction 9. 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...

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