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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

MATLAB Tools for Recommender Systems

A recommender system is a model that’s designed to anticipate the preferences of a specific user. When applied to the domain of movies, it transforms into a movie recommendation engine. The process involves filtering items in a database by predicting the user’s potential ratings and facilitating the connection of users with the most suitable content in the dataset. This holds significance because, in extensive catalogs, users might not discover all pertinent content. Effective recommendations enhance content consumption and major platforms such as Netflix heavily depend on them to maintain user engagement. In this chapter, we will learn the basic concepts of recommender systems and how to build a network intrusion detection system (NIDS) using MATLAB.

In this chapter, we’re going to cover the following main topics:

  • Introducing the basic concepts of recommender systems
  • Finding similar users in data
  • Creating...
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