In previous posts, we discussed vectors and vector norms in a basic introduction to linear algebra and got some practice working with them in our Code Lab on coding a simple recommendation system in R. Today, we'll follow up on those skills and take a first look at k-means clustering, a machine learning algorithm for clustering!
So far, we've been working with single numbers in our posts. Many kinds of data, however, can be represented by matrices. In order to discuss and learn about methods designed for data stored in matrices, today's post is a quick tutorial on getting started with linear algebra in R!
In our first code lab on estimating pi with dart throwing, we talked briefly about algorithms. If you're new to algorithms, we can think of them as recipes, or instructions, for doing or computing something. Today, we'll dive a little deeper into some aspects of algorithms.
When we shop online, we often get recommendations for other products that are similar to ones we've been looking at. Systems that recommend related products and services are frequently referred to as recommendation systems. In today's Code Lab, we'll code a simple recommendation system using something called cosine similarity!
In our last two posts, we went over how to start making data visualizations in R with ggplot2. Now that we've finished that series, let's work on a Code Lab featuring exploratory data analysis! Today, we’ll be exploring patterns in urban bike share usage...
Now that we’ve had several posts on getting started with coding in R (see Part 1, Part 2, and More Resources) we’re ready to get started with our first Code Lab! In this post, we’ll see how we can estimate pi with dart throwing in R!
