REPOGEO REPORT · LITE

llSourcell/Learn-Natural-Language-Processing-Curriculum

Default branch master · commit c02ae581 · scanned 5/9/2026, 2:42:31 AM

GitHub: 1,052 stars · 296 forks

AI VISIBILITY SCORE

17 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 0 warn · 1 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 llSourcell/Learn-Natural-Language-Processing-Curriculum, 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

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

nlp, natural-language-processing, curriculum, education, deep-learning, python, pytorch, machine-learning, ai

highlicense#2

Create a LICENSE file for the repository

Why:

COPY-PASTE FIX

(Create a LICENSE file in the repository root. Consider a permissive license like MIT or Apache-2.0, and add its full text.)

mediumhomepage#3
Set the repository homepage URL
Why:
COPY-PASTE FIX
```
http://wizards.herokuapp.com
```

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 llSourcell/Learn-Natural-Language-Processing-Curriculum

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 Deep Learning Specialization · recommended 1×
Hugging Face's NLP Course · recommended 1×
huggingface/transformers · recommended 1×
Coursera's Natural Language Processing Specialization · recommended 1×

CATEGORY QUERY
Looking for a structured curriculum to learn natural language processing from scratch.
you: not recommended
AI recommended (in order):
1. Coursera's Deep Learning Specialization
2. Hugging Face's NLP Course
3. Hugging Face Transformers library (huggingface/transformers)
4. fast.ai's Practical Deep Learning for Coders
5. Coursera's Natural Language Processing Specialization
6. deeplearning.ai
7. NPTEL's Natural Language Processing
8. NPTEL website
9. Stanford's CS224N: Natural Language Processing with Deep Learning
AI recommended 9 alternatives but never named llSourcell/Learn-Natural-Language-Processing-Curriculum. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find an intensive program to master NLP using Python and deep learning?
you: not recommended
AI recommended (in order):
1. DeepLearning.AI's Natural Language Processing Specialization on Coursera
2. fast.ai's Practical Deep Learning for Coders
3. Stanford University's CS224N: Natural Language Processing with Deep Learning
4. Udemy - Natural Language Processing with Deep Learning in Python
5. edX - Microsoft Professional Program in AI
AI recommended 5 alternatives but never named llSourcell/Learn-Natural-Language-Processing-Curriculum. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
fail
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 llSourcell/Learn-Natural-Language-Processing-Curriculum?
pass
AI did not name llSourcell/Learn-Natural-Language-Processing-Curriculum — 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 llSourcell/Learn-Natural-Language-Processing-Curriculum in production, what risks or prerequisites should they evaluate first?
pass
AI named llSourcell/Learn-Natural-Language-Processing-Curriculum 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 llSourcell/Learn-Natural-Language-Processing-Curriculum solve, and who is the primary audience?
pass
AI did not name llSourcell/Learn-Natural-Language-Processing-Curriculum — 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?

Embed your GEO score

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MARKDOWN (README)

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llSourcell/Learn-Natural-Language-Processing-Curriculum — 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