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MATLAB for Machine Learning

MATLAB for Machine Learning

By : Giuseppe Ciaburro
4.8 (4)
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MATLAB for Machine Learning

MATLAB for Machine Learning

4.8 (4)
By: Giuseppe Ciaburro

Overview of this book

Discover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications. By navigating the versatile machine learning tools in the MATLAB environment, you’ll learn how to seamlessly interact with the workspace. You’ll then move on to data cleansing, data mining, and analyzing various types of data in machine learning, and visualize data values on a graph. As you progress, you’ll explore various classification and regression techniques, skillfully applying them with MATLAB functions. This book teaches you the essentials of neural networks, guiding you through data fitting, pattern recognition, and cluster analysis. You’ll also explore feature selection and extraction techniques for performance improvement through dimensionality reduction. Finally, you’ll leverage MATLAB tools for deep learning and managing convolutional neural networks. By the end of the book, you’ll be able to put it all together by applying major machine learning algorithms in real-world scenarios.
Table of Contents (17 chapters)
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1
Part 1: Getting Started with Matlab
4
Part 2: Understanding Machine Learning Algorithms in MATLAB
9
Part 3: Machine Learning in Practice

Summary

In this chapter, we have gained valuable insights into performing accurate classification tasks within the MATLAB environment. We began by delving into the realm of decision tree methods, where we familiarized ourselves with key concepts such as nodes, branches, and leaf nodes. By repeatedly dividing records into homogeneous subsets based on the target attribute, we learned how to classify objects into distinct classes effectively. Moreover, we explored the prediction aspect of SVMs, which are particularly effective in solving complex problems with a clear margin of separation between classes. SVMs can handle both linearly separable and non-linearly separable data by transforming the input space into a higher-dimensional feature space.

In the subsequent section, our focus shifted toward conducting precise regression analysis within the MATLAB environment. We commenced by delving into simple linear regression, gaining an understanding of its definition and the process of...

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