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:
HOMEWORK
🏠 1st assignment spreadsheet:
🏠 Submit all other assignments to Radu’s Discord Server:
GITHUB LINKS
💻 Drawing App:
💻 Data:
💻 Custom Chart Component:
💻 Full Course Code (In Parts):
PREREQUISITES
🎥 Interpolation:
🎥 Linear Algebra:
🎥 Trigonometry:
LINKS
🔗 Check out the Recognizer we’ll build in this course:
🔗 Draw for Radu, Call for help video:
🔗 Draw for Radu, Data collection tool:
🔗 Radu’s Self-driving Car Course:
🔗 Radu’s older Machine Learning video:
🔗 CHART TUTORIAL (mentioned at 01:45:27):
🔗 CHART CODE:
TOOLS
🔧 Visual Studio Code:
🔧 Google Chrome:
🔧 Node JS:
(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
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