A recommendation engine (sometimes referred to as a
recommender system) is a tool that lets algorithm developers predict
what a user may or may not like among a list of given items. Recommender
systems have become extremely common in recent years, and are applied
in a variety of applications. The most popular ones are movies, music,
news, books, research articles, search queries, social tags, and
products in general.
This video starts with an introduction to recommendation systems and
its applications. You will then start building recommendation engines
straight away from the very basics. As you move along, you will learn to
build recommender systems with popular frameworks such as R, Python,
and more. You will get an insight into the pros and cons of different
recommendation engines and when to use which recommendation.
With the help of this course, you will quickly get up and running
with Recommender systems. You will create recommendation engines of
varying complexities, ranging from a simple recommendation engine to
real-time recommendation engines.
About The Author
Suresh Kumar Gorakala is a Data scientist focused on Artificial Intelligence. He has professional experience close to 10 years, having worked with various global clients across multiple domains and helped them in solving their business problems using Advanced Big Data Analytics. He has extensively worked on Recommendation Engines, Natural language Processing, Advanced Machine Learning, Graph Databases. He previously co-authored Building a Recommendation System with R for Packt Publishing. He is a passionate traveler and is a photographer by hobby.