Ensemble Methods in Machine Learning for beginners

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Data Science Webinars

Ensemble Methods in Machine Learning for beginners

August 10, 2019|Sayak Dutta

Sayak is currently working as a Data Scientist with Amazon.com, focussed on building data science and machine learning solutions for Amazon’s Last Mile business. He has more than four years of experience in data science and advanced analytics, with expertise in developing end-to-end predictive models and statistical solutions to address business problems in retail, manufacturing and supply chain. Sayak has previously worked as a Senior Consultant with Capgemini – Data Sciences and Analytics Group and as a Data Scientist with Landmark Group – Analytics CoE. He holds a Master’s in Economics from the Madras School of Economics, Chennai.

Key takeaways for you

  • Theoretical understanding of Machine Learning – Supervised, Unsupervised Concepts, Classification and Regression.
  • Theoretical understanding of Decision Trees, Node Splitting and various algorithms used to build decision trees.
  • Concepts of Ensemble Learning – Boosting, Bagging and Stacking.
  • Understanding the math behind each of the aforementioned concepts.
  • Hands on model building – RF, XGboost, GBM, Model Stacking using Python.

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