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Prediction or Classification Using Ensemble Methods

In this project, we are going to implement machine learning algorithm as part of Ensemble exercise.
Event Date
27th
Oct - 2017
06:00pm - 08:30pm PST
28th
Oct - 2017
06:00pm - 08:30pm PST
What are the prerequisites for this project?
  • This project assumes that you have a good knowledge of Data Science and the R language. If not - we recommend you to take the Data Science in R Programming course first.

What will you learn

  • Where and why to apply ensemble methods
  • Bagging, Boosting models implementation
  • Stacking model as ensemble model
  • Implementation and inferences
  • Hands-on example using a standard sample dataset

Project Description

Ensemble methods are learning algorithms that construct a. set of classifiers and then classify new data points by taking a vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting. In the age of artificial intelligence and machine learning the ensemble, methods are becoming new norms, as a stand-alone model won't be sufficient to capture the dynamics of the data variability.

Instructors

 
Pradeepta

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