Implementing Machine Learning Principle Using Spark

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There are no scheduled dates for this course.

  Available by Request


The course deals with the basic principles needed to understand and apply Machine Learning models and methods using Spark. The topics include Supervised and Unsupervised Learning, Bayesian Decision Theory, Parametric Methods, Tuning Model Complexity, Dimensionality Reduction, Clustering, Nonparametric Methods, Decision Trees, Comparing and Combining Algorithms, as well as a few other applications of these methods.

There are no prerequisites for this course.

4 Days/Lecture & Lab

This course is designed for those wanting to learn the basic principles needed to understand and apply Machine Learning models and methods using Spark.

Fundamentals of Spark – 1

  • Fundamentals of Spark – 2
  • Implementing Machine Learning Algorithm in Spark – 1
  • Implementing Machine Learning Algorithm in Spark – 2




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