Building Practical Recommendation Engines – Part 2

HDRips | MP4/AVC, ~81 kb/s | 1280x720 | Duration: 02:12:31 | English: AAC, 128 kb/s (2 ch) | 521 MB

Genre: Development / Programming

Use behavioral and historical data to predict the future.

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.