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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 embarked on an exciting journey into the world of ML, exploring a range of popular algorithms to find the best fit for our specific needs. We learned the importance of conducting a preliminary analysis to determine the most suitable algorithm and gained insights into the step-by-step process of building ML models.

Furthermore, we delved into the powerful capabilities of MATLAB for ML, including its support for classification, regression, clustering, and deep learning tasks. We discovered the convenience of using MATLAB apps for automated model training and code generation, streamlining our workflow.

We also introduced the Statistics and Machine Learning Toolbox and the Deep Learning Toolbox, which provided us with additional tools and functionalities to solve our specific problems. We recognized the significance of statistics and algebra in the field of ML and understood how MATLAB could assist us in leveraging these concepts effectively.

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