REPOGEO REPORT · LITE

dair-ai/ML-Course-Notes

Default branch main · commit 15fd0a13 · scanned 5/10/2026, 3:52:44 PM

GitHub: 6,451 stars · 858 forks

AI VISIBILITY SCORE

22 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

1 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface dair-ai/ML-Course-Notes, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition README opening to clarify role as course notes

Why:

CURRENT

# 🎓 Machine Learning Course Notes
A place to collaborate and share lecture notes on all topics related to machine learning, NLP, and AI.

COPY-PASTE FIX

# 🎓 Machine Learning Course Notes
A curated, collaborative collection of high-quality lecture notes and summaries for popular machine learning, deep learning, and NLP courses (e.g., Andrew Ng's ML Specialization). This repository serves as a supplementary learning resource, not a course itself.

highhomepage#2
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://dair.ai/ml-course-notes
```

mediumreadme#3

Add a clear statement about the repository's license(s) to the README

Why:

COPY-PASTE FIX

## License
This repository's content is governed by the terms outlined in the [LICENSE](LICENSE) file. Please review the file to understand the specific usage rights and restrictions that apply to these notes.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface dair-ai/ML-Course-Notes

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

fast.ai's Practical Deep Learning for Coders

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

fast.ai's Practical Deep Learning for Coders · recommended 2×
Coursera's Machine Learning by Andrew Ng · recommended 1×
Stanford CS229: Machine Learning · recommended 1×
Caltech CS 156: Learning from Data · recommended 1×
University of Washington's Machine Learning Specialization · recommended 1×

CATEGORY QUERY
Where can I find detailed lecture notes for a comprehensive machine learning specialization?
you: not recommended
AI recommended (in order):
1. Coursera's Machine Learning by Andrew Ng
2. Stanford CS229: Machine Learning
3. fast.ai's Practical Deep Learning for Coders
4. Caltech CS 156: Learning from Data
5. University of Washington's Machine Learning Specialization
6. MIT 6.036: Introduction to Machine Learning
AI recommended 6 alternatives but never named dair-ai/ML-Course-Notes. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for structured learning resources to understand deep learning and natural language processing concepts.
you: not recommended
AI recommended (in order):
1. Coursera's Deep Learning Specialization by Andrew Ng
2. fast.ai's Practical Deep Learning for Coders
3. PyTorch
4. Hugging Face's NLP Course
5. Hugging Face Transformers library
6. Stanford's CS224n: Natural Language Processing with Deep Learning
7. Google's Machine Learning Crash Course
8. TensorFlow
9. edX's Microsoft Professional Program in AI
10. Manning Publications' 'Deep Learning with Python'
11. Keras
AI recommended 11 alternatives but never named dair-ai/ML-Course-Notes. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of dair-ai/ML-Course-Notes?
pass
AI did not name dair-ai/ML-Course-Notes — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts dair-ai/ML-Course-Notes in production, what risks or prerequisites should they evaluate first?
pass
AI named dair-ai/ML-Course-Notes explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo dair-ai/ML-Course-Notes solve, and who is the primary audience?
pass
AI did not name dair-ai/ML-Course-Notes — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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dair-ai/ML-Course-Notes — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite