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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

Deploying machine learning models

Deploying machine learning models refers to the process of making a trained model available for making predictions on new, unseen data. It involves taking the trained model and integrating it into a production environment where it can receive input data, perform predictions, and return the results. The trained model needs to be organized and packaged into a format suitable for deployment. This may involve exporting the model into a file format that can be easily loaded and used by other systems. An application programming interface (API) is typically created to expose the machine learning model’s functionality. The API acts as the interface that other systems or applications can use to send data and receive predictions from the model.

If the model is expected to handle many concurrent requests, the deployment environment may need to be scaled to accommodate the increased load. This may involve setting up clusters of servers or using cloud...

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MATLAB for Machine Learning
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