This online course focuses on the fundamentals of computer programming (variables, conditionals, iteration, functions & objects) using JavaScript. In particular it leverages the p5.js creative computing environment which is oriented towards visual displays on desktops, laptops, tablets or smartphones. The course is designed for computer programming novices. This online course focuses on the fundamentals of computer programming (variables, conditionals, iteration, functions & objects) using JavaScript. In particular it leverages the p5.js creative computing environment which is oriented towards visual displays on desktops, laptops, tablets or smartphones. The course is designed for computer programming novices.
This course is for aspiring developers who want to learn how to work with data in web applications. How do you retrieve, collect, and store data? The course will be taught through a series of creating three data projects. The first will be client-side only and examine how to load data with fetch() and present on a web page. Viewers will learn about handling asynchronous events with Promises and how to render data to the DOM as well as draw to HTML5 canvas with p5.js. The second and third project will introduce "full stack" development adding server-side programming with node.js for data persistence and API authentication.
This video series is designed to teach you the basics of working with git version control and the GitHub website. You will understand the concept of version control and the difference between git software and GitHub the website. Videos cover terminology like branch, fork, merge, pull, push, and remote. You will get a chance to make your first pull request to a git repository on GitHub.
Welcome to “A Beginner's Guide to Machine Learning in JavaScript”! In this series, I'll teach the concepts behind machine learning using the ml5.js library.
Can the unpredictable evolutionary and emergent properties of nature be captured in software? Can understanding the mathematical principles behind the physical world help to create digital worlds? This learning playlist focuses on the programming strategies and techniques behind computer simulations of natural systems. I'll explore topics ranging from basic mathematics and physics concepts to more advanced simulations of complex systems. Subjects covered include physics simulation, trigonometry, self-organization, genetic algorithms, and neural networks. This track accompanies https://natureofcode.com/
This side track explores patterns found in nature. We'll cover how to simulate growing plants, flowers, and trees in p5.js and Processing. Fractal trees and L-systems galore! By the end of this track, you'll be able to code your own digital garden!
A collection of videos exploring the new features and capabilities introduced in p5.js 2.0, including loading with async and await, fonts and typography, custom shapes and curves, and more!
Pi, which is the ratio of a circle's circumference to its diameter, is one of the most important mathematical constants. This track is a compilation of the many challenges I have completed to calculate Pi.
Take a ride along the Pixels track and explore pixels with p5.js and Processing. In this track, I demonstrate how to work with real-time live video, using tint() to change colors and copy() to takes snapshots. I also explore how the pixel array works. I create examples of "software" mirrors that draw pixels as shapes or use DOM elements. I demonstrate how to create a "painting" with particles smearing colors from pixels, and build a slitscan effect.
This track is a compilation of the many Snowflakes challenges that I have done during my annual fundraiser for the Processing Foundation. Donations can be made at https://processing.org/donate/. Thank you!
This side track is a collection of videos related to supershapes that use spherical coordinates and superformulas. While not technically a “supershape”, the mandelbulb is constructed in a similar manner and is a SUPER shape.
In these videos I demonstrate how to train image and sound classification machine learning models and deploy those models to a web application using the p5.js and ml5.js JavaScript libraries.
