About the Course

DataHack Summit 2019 was a grand success as it broke all previous records. The conference saw over 1200 attendees, 30+ hack sessions, 70+ talks, 8 workshops and a whole lot more learning and fun!

The conference featured top thought leaders in Artificial Intelligence and Machine Learning, and the top Data Scientists and Machine Learning Engineers from across the globe. A wide variety of topics were covered. Here’s a word cloud illustrating this:

These videos are of all three days of the conference and are exclusively available only to the conference attendees until our next conference in 2020.


Course curriculum

  • 1
    Day 1- 13 November 2019
    • Auditorium 1 - Power Session- Role of Latent Variables in Machine Learning by Dr. Sarabjot Singh
    • Auditorium 2- Power Session - Identifying the Operational and Transitional States of a Machine by Anurag Sahay
    • Auditorium 3- Power Session - Personalisation is not just about Recommendation Engines, it's Much More! by Ujjyaini Mitra
    • Auditorium 3- Power Session - Tackling Real World Optimization Problems using AI by Varun Khandelwal
    • AI&ML Blackbelt Plus Program (Sponsored)
  • 2
    Day 2- 14 November 2019
    • Auditorium 1- Power Session - Framework to Manage End to End ML Projects by Kiran R
    • Auditorium 1- Power Session - Performing Machine Learning in Few KBs of RAM by Prateek Jain
    • Auditorium 2- Hack Session - Evaluating ML Models for Bias – Build an Interpretable Model using a Financial Dataset by Rajesh Jeyapaul & Prateek Goyal
    • Auditorium 2- Power Session - A Closer Look at Essential Data Sources in FinTech by Ratnakar Pandey & Wasimakram Binnal
    • Auditorium 3- Hack Session - Building an End-to-End Credit Risk Model by Arihant Jain
  • 3
    Day 3 - 15 November 2019
    • Auditorium 2- Hack Session - Using Genetic Algorithms to build Machine Learning Pipelines by Sahil Verma
    • Auditorium 3- Hack Session - MLOps - Putting ML Models to Production by Akash Tandon
    • Copy of Auditorium 1 - Panel Discussion - Why do 85% of AI Projects Fail?