Machine Learning with Python: Decision Trees

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Frederick Nwanganga

1:13:53

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01 - Introduction

01 - Making decisions with Python.mp4

00:51

02 - What you should know.mp4

01:10

03 - The tools you need.mp4

01:15

04 - Using the exercise files.mp4

01:46

02 - 1. Decision Trees

01 - What is a decision tree.mp4

04:29

02 - How is a classification tree built.mp4

06:27

03 - How do classification trees measure impurity.mp4

05:48

04 - How is a regression tree built.mp4

05:40

05 - How to prune a decision tree.mp4

07:10

06 - Why and when to use a decision tree.mp4

02:59

03 - 2. Working with Classification Trees

01 - How to build a classification tree in Python.mp4

06:05

02 - How to visualize a classification tree in Python.mp4

04:18

03 - How to prune a classification tree in Python.mp4

05:09

04 - 3. Working with Regression Trees

01 - How to build a regression tree in Python.mp4

07:50

02 - How to visualize a regression tree in Python.mp4

05:26

03 - How to prune a regression tree in Python.mp4

05:47

05 - Conclusion

01 - Next steps with decision trees.mp4

01:43

Description

Decision trees are one of the most common approaches used in supervised machine learning. Building a decision tree allows you to model complex relationships between variables by mimicking if-then-else decision-making as a naturally occurring human behavior. In this course, instructor Frederick Nwanganga gives you an overview of how to collect, explore, and transform your data in preparation for building decision tree models in Python.

Discover the power of decision trees, what they are, how they are built, and how they quantify impurity within a partition. Get tips from Frederick on building, visualizing, pruning, and using a decision tree in Python including classification trees and regression trees. By the end of this course, you’ll be ready to start making your own models and applying them to different domains.

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Python

Machine Learning

Frederick Nwanganga

Instructor's Courses

Information Technology leader with over 15 years of experience in both private sector and higher education. Skilled in managing highly talented and technical staff. Subject matter competence in Data Analytics, Machine Learning and Optimizations for Big Data Applications.

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