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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

Making predictions with regression analysis in MATLAB

Having explored numerous instances of linear regression, we can confidently assert that we comprehend the underlying mechanisms of this statistical method. Non-linear regression is used to model the relationship between a dependent variable and one or more independent variables when the relationship is not linear. In contrast to linear regression, where the relationship is assumed to be a straight line, non-linear regression allows for more complex and flexible relationships between variables.

Up until now, we have exclusively employed continuous variables as predictors. However, what transpires when the predictors are categorical variables? No need to fret, as the fundamental principles of regression techniques remain unchanged.

Multiple linear regression with categorical predictor

Categorical variables differ from numerical ones as they do not stem from measurement operations but rather from classification and comparison...

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