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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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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

Data preparation

Once data collection has been completed and imported into MATLAB, it is finally time to start the analysis process. This is what a novice might think; conversely, we must first proceed to the preparation of data (data wrangling). This is a laborious process that can take a long time, in some cases about 80 percent of the entire data analysis process. However, it is a fundamental prerequisite for the rest of the data analysis workflow, so it is essential to acquire the best practices in such techniques.

Before submitting our data to any machine learning algorithm, we must be able to evaluate the quality and accuracy of our observations. If we cannot access the data stored in MATLAB correctly or if we do not know how to switch from raw data to something that can be analyzed, we cannot go ahead.

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