Machine learning might sound complicated, but with the help of p5.js, we can make interactive visualizations to help wrap our head around these algorithms. In this session, we make a visualization of the popular K-Nearest Neighbor algorithm. Join us as we discuss how this algorithm can be applied to real data and track down bugs in our code. Finally, with help from Jiwon, our Creative Coding guru, we investigate how we might turn this algorithm into an art project.
Here are some Stack Overflow questions related to the work we did in this session:
Using scikit-learn's KNN Implementation
Supervised Learning vs Unsupervised Learning
KNN as a Lazy Learning Algorithm
If you enjoyed this lesson, you can catch up on the rest of the series on YouTube. If you’d like to watch a session live, follow the Codecademy YouTube channel. Finally, if you want even more Creative Coding with p5.js content, you can sign up for the interactive course this series was based on here. This course was developed by Jiwon and has many more quizzes, projects, and helpful articles that we can’t fit into our streams!