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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

Classification Learner app

In the preceding paragraphs, we have faced several classification problems using some of the algorithms available in the MATLAB environment. We did it in programming mode, deliberately in order to understand in detail the different procedures. Now that we have fully understood these concepts, we can relax and explore the tools MATLAB offers us to perform interactive classification with the use of the Classification Learner app.

Using this app, we can classify our data using various algorithms and compare the results in the same environment. Select features, specify validation schemes, train models, and assess results becomes extremely simple and automatic with this app. The classification models available are: decision trees, discriminant analysis, Support Vector Machines (SVM), logistic regression, nearest neighbors, and ensemble classification.

The...

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