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Decision Trees

Description

Dive deeper into the world of machine learning by learning how to construct and interpret decision trees with our Decision Trees course. In this course, you’ll build a decision tree implementation from the ground up.

You’ll learn about concepts such as entropy, information gain, an error metric known as Area Under the Curve (AUC), and the ID3 algorithm. You’ll also get an introduction to random forests and learn to reduce overfitting with random forests. And you’ll learn to ensemble decision trees to improve prediction quality.Read more.

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The techniques and tools covered in Decision Trees are most similar to the requirements found in Data Scientist job advertisements.

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