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This course describes tree-based algorithms for regression and classification. These methods are simple, intuitive and, more importantly, highly "visual". A deeper understanding of tree-based models provides a successful tool to comprehend advanced machine learning algorithms. By the end of this course, you will learn how to build a decision tree and understand their advantages and limitations.
Duration
6 hours
Difficulty
professional
INE's world-class IT trainingInstructor for this course
Kristin Day
This course is composed by the following modules
INE's world-class IT training
Module 1
Course Introduction3 videos
Course Introduction
Initial Vocabulary
Decision Tree Pros and Cons
Module 2
Classification Models6 videos
Decision Tree Classification Intro
Types of Decision Trees
Model Selection
Feature Engineering
Measures of Impurity - Gini
Measures of Impurity - Entropy
Module 3
Model Tuning and Evaluation6 videos
Model Tuning - Part 1
Model Tuning - Part 2
Model Pre-pruning
Model Post-pruning
Visualizing and Explaining Results
Mushroom Classification
Module 4
Regression Models5 videos
Decision Tree Regression Intro
Tuning Regression Models
Pruning Regression Models
Boston House Prices Regression
Visualizing the Boston Regression Tree
Module 5
Goodbye1 video
Course Conclusion
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