Introduction, Regression/Classification, Cost Functions, and Gradient Descent
Machine learning (ML) has received a lot of attention recently, and not without good reason. It has already revolutionized fields from image recognition to healthcare to transportation. Yet a typical explanation for machine learning sounds like this:
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.”
Not very clear, is it? This post, the first in a series of ML tutorials, aims to make machine learning accessible to anyone willing to learn. We’ve designed it to give you a solid understanding of how ML algorithms work as well as provide you the knowledge to harness it in your projects.
Perceptrons, Logistic Regression, and SVMs
In this post we’ll talk about one of the most fundamental machine learning algorithms: the perceptron algorithm. This algorithm forms the basis for many modern day ML algorithms, most notably neural networks. In addition, we’ll discuss the perceptron algorithm’s cousin, logistic regression. And then we’ll conclude with an introduction to SVMs, or support vector machines, which are perhaps one of the most flexible algorithms used today.