Machine Learning & Neural Networks without Libraries – No Black Box Course

Welcome to this No Black Box Machine Learning Course in JavaScript. It’s a course where we code without using libraries because it’s the best way to learn all inner workings of a machine learning system and you’ll greatly improve your software development skills as well.

The goal in this course is to build a web app that learns to recognize drawings. This is phase 2, where we increase the accuracy of the method we developed in Phase 1. We do this by implementing more sophisticated features and using other classification methods (like the Neural Network). In Phase 2 we also learn about Data Cleaning, Confusion Matrices, Geometry and the difference between Vector and Raster data (pixels).

🎥 No Black Box Phase 1 Course: https://youtu.be/vDDjtwQDw2k

✏️ Course created by @Radu (PhD in Computer Science)

📁 Data: https://github.com/gniziemazity/drawing-data
💻 Code: https://github.com/gniziemazity/ml-course-phase-2
💻 Ilya's code: https://gist.github.com/id-ily....ch/8630fb273e5c5a0b6
💻 Neural Network Code: https://github.com/gniziemazity/neural-network
Phase 3 Poll: https://forms.office.com/e/QTMCLLaV24

⭐️ Other Resources ⭐️
Recognizer we build in this course: https://radufromfinland.com/projects/ml/recognizer
Euclidean Distance Video: https://youtu.be/3rPwfmrCwVw
Interpolation Video: https://youtu.be/J_puRs40GhM
Draw the Portal Game Tutorial (Inspired from Dr. Strange): https://youtu.be/0SxiyLk2IMM
Why the Circle has the Largest Area: https://youtu.be/CFBa2ezTQJQ
Recognizing drawings via webcam: https://youtu.be/QXB1ytG95gs
Self-driving Car Course: https://youtu.be/Rs_rAxEsAvI
Discord Server: https://discord.com/invite/gJFcF5XVn9
Scikit-learn documentation: http://scikit-learn.org/stable..../modules/generated/s

⭐️ Contents ⭐️
0:00:00 Introduction
0:04:07 Phase 1 Code Review
0:23:11 Data Cleaning
0:41:30 Confusion Matrix
1:16:00 Euclidean Distance Marker
1:16:06 Measuring the Elongation
1:39:23 Measuring the Roundness
1:59:20 Vector vs Raster (Pixels)
2:22:40 Neural Networks
3:04:49 Optimizing Neural Networks
3:25:15 Deep Neural Networks

🎉 Thanks to our Champion and Sponsor supporters:
👾 davthecoder
👾 jedi-or-sith
👾 南宮千影
👾 Agustín Kussrow
👾 Nattira Maneerat
👾 Heather Wcislo
👾 Serhiy Kalinets
👾 Justin Hual
👾 Otis Morgan
👾 Oscar Rahnama

--

Learn to code for free and get a developer job: https://www.freecodecamp.org

Read hundreds of articles on programming: https://freecodecamp.org/news