Book Image

R Machine Learning By Example

By : Raghav Bali
Book Image

R Machine Learning By Example

By: Raghav Bali

Overview of this book

Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems. This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems. You’ll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms. Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.
Table of Contents (15 chapters)
R Machine Learning By Example
About the Authors
About the Reviewer

Collaborative filters

Recommendation systems and collaborative filters share a long history. From the early days of primitive recommender engines which utilized specific categorizations with hard-coded results, to current sophisticated recommender engines on various e-commerce platforms, recommender engines have made use of collaborative filters throughout. They are not only easy to understand but are equally simple to implement. Let us take this opportunity to learn more about collaborative filters before we dive into implementation details.


Fun Fact

Recommender engines surely outdate any known e-commerce platform! Grundy, a virtual librarian, was developed in 1979. It was a system for recommending books to users. It modeled the users based upon certain pre-defined stereotypes and recommended books from a known list for each such category.

Core concepts and definitions

Collaborative filters (denoted as CF henceforth) and recommender engines in general use certain terms and definitions to...