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