Machine Learning with Python: Implementing Random Forests
In this short course we will cover the basics of decision trees and bootstrapping, and from there get into random forests -- one of the big success stories in machine learning, useful for predictive tasks in both classification and regression problems. Beyond this, attendees should leave with a basic understanding of the bias-variance trade-off, and how random forest models help us navigate through these troubled waters.
To take advantage of the hands-on portions of the lecture, it is recommended that students have installed Anaconda with Python 3.7 from https://www.anaconda.com/distribution/.