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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
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Part 3: Machine Learning in Practice

In this part, we will take a journey into the specialized domains of MATLAB application, where we will reveal the immense potential of the software in various fields.

Explore the enchanting world of natural language processing using MATLAB, where language intricacies are deciphered, enabling machines to comprehend and respond to human communication. Transitioning into the visual realm, Chapter 8 opens doors to the manipulation and understanding of visual data, bringing pixels to life through sophisticated algorithms. Delve into the intricate patterns of Chapter 9, where historical data paves the way for informed predictions and strategic decision-making. Then, witness the power of Chapter 10, where personalized suggestions and recommendations emerge from intricate algorithms, enhancing user experiences. Concluding our exploration, immerse yourself in the realm of Chapter 11, where subtle deviations in data patterns are unveiled, providing a...

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