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MATLAB for Machine Learning

You're reading from   MATLAB for Machine Learning Unlock the power of deep learning for swift and enhanced results

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781835087695
Length 374 pages
Edition 2nd Edition
Languages
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Getting Started with Matlab
2. Chapter 1: Exploring MATLAB for Machine Learning FREE CHAPTER 3. Chapter 2: Working with Data in MATLAB 4. Part 2: Understanding Machine Learning Algorithms in MATLAB
5. Chapter 3: Prediction Using Classification and Regression 6. Chapter 4: Clustering Analysis and Dimensionality Reduction 7. Chapter 5: Introducing Artificial Neural Network Modeling 8. Chapter 6: Deep Learning and Convolutional Neural Networks 9. Part 3: Machine Learning in Practice
10. Chapter 7: Natural Language Processing Using MATLAB 11. Chapter 8: MATLAB for Image Processing and Computer Vision 12. Chapter 9: Time Series Analysis and Forecasting with MATLAB 13. Chapter 10: MATLAB Tools for Recommender Systems 14. Chapter 11: Anomaly Detection in MATLAB 15. Index 16. Other Books You May Enjoy

Summary

In this chapter, we began our exploration of the MATLAB desktop and its convenient interaction features. We familiarized ourselves with the MATLAB Toolstrip, which is organized into various tabs. Subsequently, we delved into the importing capabilities of MATLAB, enabling us to read diverse types of data resources. We acquired knowledge on how to import data into MATLAB interactively and programmatically. Moreover, we comprehended the process of exporting data from the workspace and working with media files.

Next, we embarked on the challenging task of data preparation. We learned various techniques, including identifying missing values, modifying data types, replacing missing values, removing incomplete entries, organizing tables, identifying outliers, and consolidating multiple data sources. Following that, we explored exploratory statistics techniques, which enabled us to derive insightful features guiding us in selecting appropriate tools for extracting knowledge from...

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