K-Means Clustering 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.

Hadoop is a great way to work with data when your data is expanding at a rapid scale. However, to do something meaningful with data, you often need to run Machine learning algorithms. These algorithms need to be converted into MapReduce patterns which can then be implemented in Hadoop. This course will walk you through 1 such machine learning algorithm i.e. K Means clustering, and how you can implement it in Hadoop

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 the K-Means clustering 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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310+ Students
8 Lectures
1+ Hours of Video
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Loonycorn

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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