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DataHour: Ensemble Techniques in Machine Learning

Online 15-10-2022 03:00 PM to 15-10-2022 04:00 PM
  • 4830

    Registered

  • Knowledge and Learning

    Prizes

DataHour Recording

Find the resources used in the DataHour HERE.

About the DataHour:

The bias-variance trade-off is a challenge we all face while training machine learning algorithms. Ensemble methods improve model precision by using a group (or "ensemble") of models which, when combined, outperform individual models when used separately. Different methods could be used to reduce variance or bias. 

In this DataHour, Ritika will deeply explain the methods of ensembling machine learning models and their working.


Prerequisites:

Eagerness of learning data science and machine learning algorithms and basic understanding of decision trees. .

Who is this DataHour for?

  • Students & Freshers who want to build a career in the Data-tech domain.
  • Working professionals who want to transition to the Data-tech domain.
  • Data science professionals who want to accelerate their career growth


Speaker:

Ritika Wadhawan, Data Scientist at Johnson & Johnson

Ritika is a Data Scientist with 9+ years of experience in working with data analytics, machine learning, data warehousing and data visualization in business intelligence. She has worked with various prestigious companies like Johnson & Johnson, UnitedHealth Group and Aon Hewitt in numerous projects across multiple domains like HealthCare, Consumer, Digital Media, Ecommerce and Finance Management.

She has also been a mentor for the Great Learning PGPD Program in Data Science and Business Analytics for over 2 years. 


You can follow her on Linkedin

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