Social Share

ABOUT CHALLENGE

These challenges are conducted to let you work on some interesting problems solved by companies in today's world. Such a hands-on, problem-solving experience can not only help you get your next job but also teach you the latest ways of building models. This is the fourth challenge of our Machine Learning challenge streak.

This 30-day challenge is designed to help you showcase, win, and improve your Machine Learning skills by competing and learning from fellow participants.

Prizes

  • 1st prize: 700 USD *
  • 2nd prize: 500 USD *

* Cash, vouchers, or credits of an equivalent amount will be given.

Join us for an exciting webinar on Evaluating and Improving Machine Learning Models

http://blog.hackerearth.com/Webinar-Evaluating-Improving-Machine-Learning-Models

Why should you participate?

  • To learn and use the latest open-source libraries and packages
  • To learn by working on live problems because it excites you more than learning from books and tutorials!
  • To build your fan following in our community
  • Of course, grab cash prizes

Who should participate?

  • Working professionals
  • Data Science/Machine Learning enthusiasts
  • College students (if you understand basics of predictive modelling)

Tutorials

Notes

  • In order to be able to claim your prizes, your HackerEarth profile must be more than 50% complete.
  • The prizes will be disbursed before the end of December.

=======================================================================

enter image description here

Create and validate your model using the IBM Data Science Experience (DSX) platform!

IBM DSX is a powerful computational engine based on Apache Spark Executors. It has a strong computing capacity in the back end. It currently supports Python, R, and Scala.

Using IBM DSX, you can create a Python, R, or Scala, notebook-based project and create a data connection to your data source. You have options to load all types of Machine Learning algorithms that are supported by runtime from KNN and RandomForest to TensorFlow.

You can export your notebook to us for further validation after you have:

  1. Loaded your data
  2. Created data sets
  3. Modeled, trained, and validated your data

Register for IBM Cloud here : http://ibm.biz/MLChallenge
Log in to IBM DataScience Experience with your IBM Cloud credientials - https://datascience.ibm.com

Additional learning resources

Learn more by trying out some interesting code patterns on DSX here : https://developer.ibm.com/code/patterns/category/data-science/

Perform a Machine Learning Exercise:
https://developer.ibm.com/code/patterns/perform-a-machine-learning-exercise/ Correlate documents: https://developer.ibm.com/code/patterns/watson-document-correlation/

Lab - Create and deploy a scoring model to predict heart failure w/Bluemix and IBM Data Science Experience https://github.com/justinmccoy/watson-dojo-pm-tester

PRIZES

There are great prizes to be won

1st Prize

USD 700 

2nd Prize

USD 500 

GUIDELINES

  1. When the contest is running, your output will be evaluated only for 40% of the test data. After the contest is over, your output for the remaining 60% of the test ...
more
Notifications
View All Notifications