Redirecting to default login... Decision Trees and Random Forests – Mu Sigma University Learning Management System

Course

Decision Trees and Random Forests

This course will enable learners to be accustomed with the concept of Decision Tree and related techniques. At the end of this course learners will also be able to understand how to use ensemble models for solving business problems.

free
Machine Learning
Math
Self-learning

4 Lessons

Outcomes By the end of the course, learners will be familiar with the following: How a decision tree works The necessity to prune the tree Ensemble methods like Bagging, Random Forest, Boosting The difference between various decision tree methods	Level:	3
	Duration:	3 hours
	Pre-requisites:	Classification: Level 01 Refer Topics on Mathematics
	What’s next:

Decision Trees and Random Forests

Outcomes

By the end of the course, learners will be familiar with the following:

Introduction to Decision Trees and pruning trees

Bagging and Random Forest

Boosting

Rate your experience