Use behavioral and historical data to predict the future
About This Video
A unique guide that brings you unique projects that will enhance your skills with recommendation engines
Make insightful recommendations using various tools in the market
Filter information and build end-to-end recommendation engines with the help of Apache Spark, Neo4j, Python, R, and more
Recommendation systems allow you to gain insights into data and make a guess on what would be people's preference. It is used all over the web, be it shopping, social networking, or music. This video will teach you how to build unique end-to-end recommendation engines with various tools and enhance your skills.
You will look at various recommendation engines such as personalized recommendation engines, real-time recommendation engines, SVD recommender systems. You will also get a quick glance into the future of recommendation systems by the end of the video. During the course of the video, you will come across creating recommendation engines with R, Python, Apache Spark, Neo4j, Apache Mahout, and more. By the end of the course, you will also learn the best practices and tricks and tips to build efficient recommender systems.
Future and Phases of Recommendation Engines 07m 52s
Using Cases to Look Out for 01m 57s
Popular Methodologies 04m 46s
Building Practical Recommendation Engines – Part 2
Suresh K. Gorakala
2 hours 12 minutes
Suresh K. Gorakala
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.