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Pluralsight

Classification Using Tree Based Models

via Pluralsight

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Overview

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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.

Syllabus

  • Course Overview 1min
  • Building Decision Trees 17mins
  • Predicting Survival on the Titanic Using a Decision Tree 46mins
  • Using Ensembles of Algorithms to Overcome Overfitting 17mins
  • Predicting Survival on the Titanic Using Random Forests 14mins
  • Predicting Survival on the Titanic Using Gradient Boosted Trees 18mins

Taught by

Swetha Kolalapudi

Reviews

4.4 rating at Pluralsight based on 82 ratings

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