Fundamentals of Decision Trees in Machine Learning

Learn the fundamentals of decision trees in maching learning
Free tutorial
Rating: 3.9 out of 5 (144 ratings)
4,289 students
2hr of on-demand video
English
English [Auto]

Learn the fundamentals of decision trees in machine learning
Using the SPSS Modeler
Building a CHAID model
Using a lift and gains chart
Exploring algorithms
Building a tree interactively

Requirements

  • Basic understanding of statistics

Description

A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making.

If you're working towards an understanding of machine learning, it's important to know how to work with decision trees. This course covers the essentials of machine learning, including predictive analytics and working with decision trees. 

In this course, we'll explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables. Demonstrations of using the IBM SPSS Modeler are included so you can understand how decisions trees work. 

We'll also explore advanced concepts and details of decision tree algorithms.

This course is designed to give you a solid foundation on which to build more advanced data science skills.

Who this course is for:

  • Anyone interested in learning machine learning
  • Data science specialists
  • Developers

Instructor

Web Developer
Dave Andre
  • 4.2 Instructor Rating
  • 274 Reviews
  • 7,123 Students
  • 4 Courses

I'm an independent Web Developer and through my company TechLab Inc., I help agencies and companies build beautiful web experiences. I have a Bachelor's degree in Graphic Design and 10+ years of working with the web including many hours spent here and there.  I consider myself an expert problem solver that blends complexity with simple and elegant solutions. I specialize in JavaScript CSS HTML and Wordpress.

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