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Course Description

In this course, you will use the Maximum Likelihood Estimate (MLE) to approximate distributions from data. Using the Bayes Optimal Classifier, you will learn how the assumptions you make will impact your estimations. You will then learn to apply the Naive Bayes Assumption to estimate probabilities for problems that contain a high number of dimensions. Ultimately, you will apply this understanding to implement the Naive Bayes Classifier in order to build a name classification system.

The following course is required to be completed before taking this course:

  • Problem-Solving with Machine Learning

Faculty Author

Kilian Weinberger

Benefits to the Learner

  • Approximate distributions from data with Maximum Likelihood Estimate (MLE)
  • Use Naive Bayes Assumption to estimate probabilities from high dimensional data
  • Build a name classifier using the Naive Bayes algorithm

Target Audience

  • Programmers
  • Developers
  • Data analysts
  • Statisticians
  • Data scientists
  • Software engineers

Applies Towards the Following Certificates

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Cornell Computing and Information Science
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