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Machine Learning Algorithms

You're reading from Machine Learning Algorithms A reference guide to popular algorithms for data science and machine learning

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781785889622

Length 360 pages

Edition 1st Edition

Languages

Python

Tools

Processing

Concepts

Data Science

Table of Contents (22) Chapters

Title Page

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. A Gentle Introduction to Machine Learning FREE CHAPTER

2. Important Elements in Machine Learning

3. Feature Selection and Feature Engineering

4. Linear Regression

5. Logistic Regression

6. Naive Bayes

7. Support Vector Machines

8. Decision Trees and Ensemble Learning

9. Clustering Fundamentals

10. Hierarchical Clustering

11. Introduction to Recommendation Systems

12. Introduction to Natural Language Processing

13. Topic Modeling and Sentiment Analysis in NLP

14. A Brief Introduction to Deep Learning and TensorFlow

15. Creating a Machine Learning Architecture

Chapter 11. Introduction to Recommendation Systems

Imagine an online shop with thousands of articles. If you're not a registered user, you'll probably see a homepage with some highlights, but if you've already bought some items, it would be interesting if the website showed products that you would probably buy, instead of a random selection. This is the purpose of a recommender system, and in this chapter, we're going to discuss the most common techniques to create such a system.

The basic concepts are users, items, and ratings (or an implicit feedback about the products, like the fact of having bought them). Every model must work with known data (like in a supervised scenario), to be able to suggest the most suitable items or to predict the ratings for all the items not evaluated yet.

We're going to discuss two different kinds of strategies:

User or content based
Collaborative filtering

The first approach is based on the information we have about users or products and its target is to associate...

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