Classification Using Tree Based Models
Pluralsight
Course Summary
Classification problems are common in all domains and tree based models are very effective solutions to these problems. This course is all about tree based models, from simple decision trees, to complex ensemble learning techniques, and more.
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Course Description
Machine Learning can sound very complicated, but anyone with a will to learn can successfully apply it, if they approach it from first principles. This course, Classification Using Tree Based Models, covers a specific class of Machine Learning problems - classification problems and how to solve these problems using Tree based models. First, you'll learn about building and visualizing decision trees as well as recognizing the serious problem of overfitting and its causes. Next, you'll learn about using ensemble learning to overcome overfitting. Finally, you'll explore 2 specific ensemble learning techniques - Random Forests and Gradient boosted trees By the end of this course, you'll be able to recognize opportunities where you can use Tree based models to solve classification problems and measure how well your solution is doing.
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Course Syllabus
Course Overview- 1m 36s
—Course Overview 1m 36sBuilding Decision Trees- 17m 30s
—Recognizing Classification Problems 5m 14s
—Solving Classification Problems with Decision Trees 4m 35s
—Building a Decision Tree 7m 40sPredicting Survival on the Titanic Using a Decision Tree- 46m 58sUsing Ensembles of Algorithms to Overcome Overfitting- 17m 22sPredicting Survival on the Titanic Using Random Forests- 14m 52sPredicting Survival on the Titanic Using Gradient Boosted Trees- 18m 40s
