Developers

Leverage machine learning to amplify your social impact

“Data is abundant and cheap but knowledge is scarce and expensive.”

In the last few years, there has been a data revolution that has transformed the way we source, capture, and interact with data. From fortune 500 firms to start-ups, healthcare to fintech, machine learning and data science have become an integral part of everyday operations of most companies. Of all the sectors, the social good sector has not seen the push the other sectors have. It is not that the machine learning and data science techniques don’t work for this sector, but the lack of financial support and staff has stopped them from creating their special brand of magic here.

At HackerEarth, we intend to tackle this issue by sponsoring machine learning and data science challenges for social good.

Machine learning at HackerEarth

Even though machine learning (ML) is a new wing at HackerEarth, this is the fastest growing unit in the company. Also, over the past year, we have grown to a community of 200K+ machine learning and data science enthusiasts. We have conducted 50+ challenges across sectors with an average of 6500+ people participating in each.

The initiative

The “Machine Learning Challenges for Social Good” initiative is focused toward bringing interesting real-world data problems faced by nonprofits and governmental and non-governmental organizations to the machine learning and data science community’s notice. This is a win-win for both communities because the nonprofits and governmental and non-governmental organizations get their challenges addressed, and the machine learning and data science community gets to hone their skills while being agents of change.

Our role

HackerEarth will contribute by  

  • Working with the organizations to identify  and prepare the data set most suitable for the initiative
  • Hosting the challenge which includes the following (but not limited to)
    • Creating a problem statement with the given dataset
    • Finding support data sets if required
    • Framing the best evaluation metric to choose the winners
  • Promoting this to our developer community and inviting programmers to contribute to the cause
  • Sharing the winning approaches and ML models built, which the organizations can use
  • Sponsoring prizes

Are you a nonprofit or a governmental/non-governmental organization with a business/social problem for which primary or secondary data is available? If yes, please mail us at social@hackerearth.com. [Please use subject line “Reg: Machine Learning for Social Good | {Your Organization Name}]

Narendhiran Sundaresan

Share
Published by
Narendhiran Sundaresan

Recent Posts

Top 10 HR Competencies to Build a Strong HR Department: A Comprehensive Guide

Introduction In today's dynamic workplaces, a strong HR department is no longer a luxury –…

7 hours ago

8 Steps for Conducting a Job Tasks Analysis: A Complete Guide

Job task analysis is a crucial process for understanding the specific duties and skills required…

7 hours ago

Top 8 Sourcing Tools for Recruiters: A Comprehensive Guide

In today's competitive talent landscape, attracting top candidates requires going beyond traditional job board postings.…

7 hours ago

The 12 Most Effective Employee Selection Methods: A Comprehensive Guide

Finding the perfect fit for your team can feel like searching for a unicorn. But…

7 hours ago

12 Important Recruiting Metrics You Should Know

Recruitment forms a strong foundation to build an effective team. However, do you know if…

11 hours ago

7 Modern Performance Appraisal Methods to Boost Workforce Development

Introduction Performance appraisal has seen a tremendous change over the years. It is no longer…

1 day ago