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leehanchung/awesome-full-stack-machine-learning-courses
Curated list of publicly accessible machine learning engineering courses from CalTech, Columbia, Berkeley, MIT, and Stanford.
512 107 +0/wk
GitHub
berkeley berkeley-ai berkeley-reinforcement-learning caltech columbia-university computer-science deep-learning deep-neural-networks edx-columbiax machine-learning reinforcement-learning stanford
Trend
0
Star & Fork Trend (18 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
leehanchung/awesome-full-stack-machine-learning-courses has +0 stars this period . 7-day velocity: -0.2%.
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| Metric | awesome-full-stack-machine-learning-courses | VectorHub | FontDiffuser | neural |
|---|---|---|---|---|
| Stars | 512 | 512 | 512 | 512 |
| Forks | 107 | 133 | 53 | 20 |
| Weekly Growth | +0 | +0 | +0 | +0 |
| Language | JavaScript | Jupyter Notebook | Python | Vim Script |
| Sources | 1 | 1 | 1 | 1 |
| License | CC0-1.0 | NOASSERTION | N/A | MIT |
Capability Radar vs VectorHub
awesome-full-stack-machine-learning-courses
VectorHub
Maintenance Activity 98
Last code push 11 days ago.
Community Engagement 100
Fork-to-star ratio: 20.9%. Active community forking and contributing.
Issue Burden 70
Issue data not yet available.
Growth Momentum 30
No measurable growth in the current period (first-day cold start expected).
License Clarity 95
Licensed under CC0-1.0. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.