Recommendation Systems Via Hadoop And MapReduce

A hands-on workout in Hadoop, MapReduce and the art of thinking "parallel"

What's Inside

Course Description

Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data.

Use MaprReduce to Recommend friends in a Social Networking site: Generate Top 10 friend recommendations using a Collaborative filtering algorithm.

Recommendations play a big part in enriching the user experience for almost every service you use on a daily basis, whether it’s Amazon, Facebook, Netflix or anything else.
Collaborative filtering is one of the standard category of algorithms for generating recommendations based on user data. In this course we’ll show you how to build a recommendation system using data collected in Hadoop by implement a simple collaborative filtering algorithm.
  • Mail us about anything - anything! - and we will always reply :-)

What are the requirements?

  • You'll need an IDE where you can write Java code or open the source code that's shared. IntelliJ and Eclipse are both great options.
  • You'll need some background in Object-Oriented Programming, preferably in Java. All the source code is in Java and we dive right in without going into Objects, Classes etc
  • A bit of exposure to Linux/Unix shells would be helpful, but it won't be a blocker

What am I going to get from this course?

  • Master the art of "thinking parallel" - how to break up a task into Map/Reduce transformations
  • Use Hadoop + MapReduce to implement a Recommendations algorithm

What is the target audience?

  • Yep! Analysts who want to leverage the power of HDFS where traditional databases don't cut it anymore
  • Yep! Engineers who want to develop complex distributed computing applications to process lot's of data
  • Yep! Data Scientists who want to add MapReduce to their bag of tricks for processing data

Course Curriculum

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Janani Ravi

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

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