External

Decision trees are part of the foundation for Machine Learning. Although they are quite simple, they are very flexible and pop up in a very wide variety of situations. This StatQuest covers all the basics and shows you how to create a new tree from scratch, one step at a time.

Gradient Boost is one of the most popular Machine Learning algorithms in use. And get this, it’s not that complicated! This video is the first part in a series that walks through it one step at a time. This video focuses on the main ideas behind using Gradient Boost to predict a continuous value, like someone’s weight. We call this, “using Gradient Boost for Regression”. In the next video, we’ll work through the math to prove that Gradient Boost for Regression really is this simple. In part 3, we’ll walk though how Gradient Boost classifies samples into two different categories, and in part 4, we’ll go through the math again, this time focusing on classification.

The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)

Neural Networks are one of the most popular Machine Learning algorithms, but they are also one of the most poorly understood. Everyone says Neural Networks are “black boxes”, but that’s not true at all. In this video I break each piece down and show how it works, step-by-step, using simple mathematics that is still true to the algorithm. By the end of this video you will have a deep understanding of what Neural Networks do.

Random Forests make a simple, yet effective, machine learning method. They are made out of decision trees, but don’t have the same problems with accuracy. In this video, I walk you through the steps to build, use and evaluate a random forest.