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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

Feature selection and feature extraction using MATLAB

In MATLAB, there are several built-in functions and toolboxes that can be used for dimensionality reduction. In the next section, we will explore some practical examples of the dimensionality reduction algorithm in the MATLAB environment.

Stepwise regression for feature selection

Regression analysis is a valuable approach for understanding the impact of independent variables on a dependent variable. It allows us to identify predictors that hold greater influence over the model’s response. Stepwise regression is a variable selection method used to choose a subset of predictors that exhibit the strongest relationship with the dependent variable. There are three common variable selection algorithms:

  • Forward method: The forward method starts with an empty model, where no predictors are initially selected. In the first step, the variable showing the most significant association at a statistical level is added. In...

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