Random Forest Training

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The Random forests  are the accumulated learning method for the classification, regression and other tasks; the training focus on providing the in-depth insight on Ramdom Forest Algorithm, so that they would be able to deal with the algorithmic issues essential in machine learning. The Random Forest training basically focuses on the variable importance of the algorithm, as: the Random forests could be used to rank the importance of variables in the regression or the classification problem in the most natural way.

In the Introduction chapter of the Algorithm, the candidates come to know about the decision tree, where they will be introduced to all from bagging to random forests. Moreover, the candidates will come to learn about the properties as well, where the variable importance as well as relationship to the nearest neighbor in the algorithm is described.

Here are some of the training benefits of Random Forest:

  • The training proves to be helpful in understanding the properties of algorithms
  • The candidates learn to implement the intelligence in handling the issues related to the data set
  • The candidates learn how to measure the variable importance through permutation
  • The training is enriched with the intelligence on the general technique of bootstrap aggregating, or bagging, etc.
  • Through hands-on sessions, the candidates will learn to implement the intelligence on solving the algorithm with the complexity of a classifier to gain accuracy
Target audience

Professionals from IT field who have to deal with the logical formulas and wish to learn the ethics of Algorithmic Machine Learning


The candidates ling to opt for Random Forest certification training should have an understanding of the fundamentals of the statistics, classification and regression techniques, Language of R, as well as the basics of the machine learning

1. Single Decision Tree

  • Introduction to Machine Learning Ensembles Random Forest
  • The Single Decision Tree At a Glance
  • The Decision Making in Classification: Decision Tree

2. Rise of Ensemble Method

  • The Random Forest Algorithims

3. Practical Exercises in R on Business Case Studies

Business cases

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