Machine Learning and AI Foundations: Decision Trees

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  • Course details

    Many data science specialists are looking to pivot toward focusing on machine learning. This course covers the essentials of machine learning, including predictive analytics and working with decision trees. 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. This course is designed to give you a solid foundation on which to build more advanced data science skills.


    • Click here to view Keith McCormick’s instructor page

      Keith McCormick

      Machine Learning consultant, author, and conference speaker

      Keith McCormick is an independent data miner, trainer, speaker, and author.

      Keith is skilled at explaining complex methods to new users or decision makers at many levels of technical detail. He specializes in predictive models and segmentation analysis including classification trees, neural nets, general linear model, cluster analysis, and association rules.

    Skills covered in this course

  • Welcome

    - [Instructor] Hi my name's Keith McCormick. And I'd like to welcome you to Machine Learning Essentials: Decision Trees. I'm an independent consultant and I've been working in the areas of statistics and data mining for about 25 years now. In this class we're going to be learning about one of the most common types of predictive analytics models, decision trees. More than two third of all the projects that I've done over these many years have involved decision trees at one point or another. There are literally dozens of software packages and programming languages that allow you to build decision trees. I will be demonstrating the techniques using IBM SPSS Modeler, but you don't have to be a user of that software to benefit from the class. And you don't need experience with it before the class. Our strategy will be to explore two of the most common and easily available methods for building decision trees, CHAID and CART. CHAID and CART. They offer an interesting study of contrast so…

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