No Black Box Machine Learning Course - Learn Without Libraries

In this No Black Box Machine Learning Course in JavaScript, you will gain a deep understanding of machine learning systems by coding without relying on libraries. This unique approach not only demystifies the inner workings of machine learning but also significantly enhances software development skills.
✏️ Course created by @Radu (PhD in Computer Science)
🎥 Watch part two: • Machine Learning & Neu...
HOMEWORK
🏠 1st assignment spreadsheet: docs.google.com/spreadsheets/...
🏠 Submit all other assignments to Radu's Discord Server: / discord
GITHUB LINKS
💻 Drawing App: github.com/gniziemazity/drawi...
💻 Data: github.com/gniziemazity/drawi...
💻 Custom Chart Component: github.com/gniziemazity/javas...
💻 Full Course Code (In Parts): github.com/gniziemazity/ml-co...
PREREQUISITES
🎥 Interpolation: • Linear Interpolation (...
🎥 Linear Algebra: • Learn 2D Vectors with ...
🎥 Trigonometry: • Learn Trigonometry wit...
LINKS
🔗 Check out the Recognizer we'll build in this course: radufromfinland.com/projects/...
🔗 Draw for Radu, Call for help video: • Help me make a NEW Mac...
🔗 Draw for Radu, Data collection tool: radufromfinland.com/projects/ml
🔗 Radu's Self-driving Car Course: • Self-driving Car :: Ph...
🔗 Radu's older Machine Learning video: • Learn Machine Learning...
🔗 CHART TUTORIAL (mentioned at 01:45:27): • Build a Chart using Ja...
🔗 CHART CODE: github.com/gniziemazity/javas...
TOOLS
🔧 Visual Studio Code: code.visualstudio.com/download
🔧 Google Chrome: www.google.com/chrome
🔧 Node JS: nodejs.org/en/download
(make sure you add 'node' and 'npm' to the PATH environment variables when asked!)
TIMESTAMPS
⌨️(0:00:00) Introduction
⌨️(0:05:04) Drawing App
⌨️(0:46:46) Homework 1
⌨️(0:47:05) Working with Data
⌨️(1:08:54) Data Visualizer
⌨️(1:29:52) Homework 2
⌨️(1:30:05) Feature Extraction
⌨️(1:38:07) Scatter Plot
⌨️(1:46:12) Custom Chart
⌨️(2:01:03) Homework 3
⌨️(2:01:35) Nearest Neighbor Classifier
⌨️(2:43:21) Homework 4 (better box)
⌨️(2:43:53) Data Scaling
⌨️(2:54:45) Homework 5
⌨️(2:55:23) K Nearest Neighbors Classifier
⌨️(3:04:18) Homework 6
⌨️(3:04:49) Model Evaluation
⌨️(3:21:29) Homework 7
⌨️(3:22:01) Decision Boundaries
⌨️(3:39:26) Homework 8
⌨️(3:39:59) Python & SkLearn
⌨️(3:50:35) Homework 9