Book Image

MATLAB for Machine Learning - Second Edition

By : Giuseppe Ciaburro
Book Image

MATLAB for Machine Learning - Second Edition

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)
Free Chapter
1
Part 1: Getting Started with Matlab
4
Part 2: Understanding Machine Learning Algorithms in MATLAB
9
Part 3: Machine Learning in Practice

Introducing the basic concepts of recommender systems

A recommender system is a type of information filtering system that’s designed to suggest items or content to users based on their preferences, historical behavior, or other relevant factors. These systems are widely used in various online platforms to help users discover products, services, content, and more. Recommender systems involve two primary entities: users and items. Users are individuals for whom recommendations are generated, and items are the products, content, or services to be recommended. These items can include movies, books, products, news articles, and more.

Recommender systems rely on data that captures the interaction between users and items. This interaction data can include user ratings, purchase history, clicks, views, likes, and any other form of user engagement with items.

There are different types of recommender systems:

  • Collaborative filtering (CF): CF methods make recommendations...