Uploaded February 2023 | Updated September 2026, 2 weeks ago
In this video I'll teach you how to visualize machine learning data using JavaScript. We visualize the data I collected from you when I made this call for help: youtu.be/Yw2QZ1vq2ek
The data is just JSON files like the one we generated in the last lesson. Each of these contain the session id, student name and 8 different drawings. At the moment there are 496 files and you can get them from Github. The number is still growing and I’ll be updating it from time to time.
📁 Data
github.com/gniziemazity/drawing-data
Now the drawings are grouped together by student and that’s not convenient for Machine Learning in the long run, so, I’ll teach you how to process this raw data into a more manageable form: a dataset where each drawing is a sample. I’ll also teach you how to make a data visualizer app, but the processing we do at first doesn’t really need an interface. So, I’ll be working with Node JS, a back-end environment for JavaScript.
To follow along, install Node JS:
nodejs.org/en/download
Make sure you add 'node' and 'npm' to the PATH environment variable when asked!
⭐️HOMEWORK⭐️
Style the data viewer page in a different way and share your updates on my Discord server:
discord.com/invite/gJFcF5XVn9
I'll choose my favorites and showcase them in a future video.
Draw for me :-) I will use your data in the future:
Call for help video: youtu.be/Yw2QZ1vq2ek
Data collection tool: radufromfinland.com/projects/ml
💻 Code
github.com/gniziemazity/ml-course
✔️ Use P1 to follow along
✔️ P2 is the code after this lesson
⭐️TIMESTAMPS⭐️
00:00 Introduction
01:26 Processing data with NodeJS
10:04 Drawing PNG images with NodeJS
15:26 Backwards compatibility with the Drawing App
20:05 Data Viewer Web App
30:49 Styling the page
In this video I'll teach you how to visualize machine learning data using JavaScript. We visualize the data I collected from you when I made this call for help: youtu.be/Yw2QZ1vq2ek
The data is just JSON files like the one we generated in the last lesson. Each of these contain the session id, student name and 8 different drawings. At the moment there are 496 files and you can get them from Github. The number is still growing and I’ll be updating it from time to time.
📁 Data
github.com/gniziemazity/drawing-data
Now the drawings are grouped together by student and that’s not convenient for Machine Learning in the long run, so, I’ll teach you how to process this raw data into a more manageable form: a dataset where each drawing is a sample. I’ll also teach you how to make a data visualizer app, but the processing we do at first doesn’t really need an interface. So, I’ll be working with Node JS, a back-end environment for JavaScript.
To follow along, install Node JS:
nodejs.org/en/download
Make sure you add 'node' and 'npm' to the PATH environment variable when asked!
⭐️HOMEWORK⭐️
Style the data viewer page in a different way and share your updates on my Discord server:
discord.com/invite/gJFcF5XVn9
I'll choose my favorites and showcase them in a future video.
Draw for me :-) I will use your data in the future:
Call for help video: youtu.be/Yw2QZ1vq2ek
Data collection tool: radufromfinland.com/projects/ml
💻 Code
github.com/gniziemazity/ml-course
✔️ Use P1 to follow along
✔️ P2 is the code after this lesson
⭐️TIMESTAMPS⭐️
00:00 Introduction
01:26 Processing data with NodeJS
10:04 Drawing PNG images with NodeJS
15:26 Backwards compatibility with the Drawing App
20:05 Data Viewer Web App
30:49 Styling the page








![Detecting Colored Markers with JavaScript [Understanding AI - Lesson 13 / 15]
PLAYLIST: https://www.youtube.com/playlist?list=PLB0Tybl0UNfYbL1vDNrfHoYOKf1Sn0A81
Remember those early mobile games where you could steer by tilting the phone? In this video, Ill guide you through using the device orientation sensor to capture tilt movements. Its a fantastic technique that opens up a world of possibilities for game control.
Lets explore the potential of this feature not just for games, but for other applications too. Ill share my friends creation, Jazzman vs. Metalheadz, which showcases the versatility of tilt controls.
The applications extend beyond gaming – think about measuring tree heights or distances to distant objects. If youre interested, I have tutorials on those topics as well. Towards the end of the video, Ill demonstrate how to integrate tilt controls into our racing game, tying it back to the Understanding AI course.
So, let me steer you in the right direction for implementing device orientation in your projects. And by the way, did I mention well be doing some nononooooo again? You wont want to miss it! Like, share, and subscribe for more exciting tutorials on AI, game development, and innovative programming techniques!
💬DISCORD💬
discord.gg/gJFcF5XVn9
⭐LINKS⭐
Self-driving Car Course: https://www.youtube.com/playlist?list=PLB0Tybl0UNfYoJE7ZwsBQoDIG4YN9ptyY
My Videos with Markers: https://www.youtube.com/playlist?list=PLB0Tybl0UNfb2mpLdkgVNRHC1VCMGthq_
Coding my Personal Avatar: https://youtu.be/U88NM8ScsPY
Summary of the Avatar Project: https://youtu.be/JFMTudIJLH0
#ImageProcessing #JavaScriptImageProcessing #JavaScriptMarkerDetector #JavaScriptMarkers
⭐TIMESTAMPS⭐
00:00 Introduction
01:21 Basic Project Setup
02:43 Accessing the Camera
04:45 Image Processing
08:10 Getting the ImageData
13:38 Measuring Blueness of Pixels
21:43 Visualizing Pixel Properties
26:30 Thresholding
28:41 Marker Location and Size
32:49 Two Markers Detecting Colored Markers with JavaScript [Understanding AI - Lesson 13 / 15]](https://i.ytimg.com/vi/jy-Mxbt0zww/mqdefault.jpg)

