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MATLAB for Machine Learning

MATLAB for Machine Learning - Second Edition

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

Using ML techniques

In the previous section, we explored the various types of ML paradigms in detail. So, we have understood the basic principles that underlie the different approaches. At this point, it is necessary to understand what the elements that allow us to discriminate between the different approaches are; in other words, in this section, we will understand how to adequately choose the learning approach necessary to obtain our results.

Selecting the ML paradigm

Selecting the appropriate ML algorithm can feel overwhelming given the numerous options available, including both supervised and unsupervised approaches, each employing different learning strategies.

There is no universally superior method, nor one that fits all situations. In large part, the search for the right algorithm involves trial and error; even seasoned data scientists cannot determine whether an algorithm will work without testing it. Nonetheless, the algorithm choice is also influenced by factors...

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MATLAB for Machine Learning
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