REPOGEO REPORT · LITE

humphd/have-fun-with-machine-learning

Default branch master · commit d0418772 · scanned 5/23/2026, 12:33:17 PM

GitHub: 5,114 stars · 534 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 humphd/have-fun-with-machine-learning, 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 the README's opening to explicitly state its purpose as a learning guide, not a framework

Why:

CURRENT

# Have Fun with Machine Learning: A Guide for Beginners
Also available in [Chinese (Traditional)](README_zh-tw.md).

COPY-PASTE FIX

# Have Fun with Machine Learning: A Guide for Beginners
This repository offers a practical, step-by-step learning path for developers new to AI, focusing on understanding core concepts rather than providing a production-ready framework.
Also available in [Chinese (Traditional)](README_zh-tw.md).

mediumabout#2
Add a homepage URL to the repository's 'About' section
Why:
COPY-PASTE FIX
```
https://github.com/humphd/have-fun-with-machine-learning
```

lowreadme#3

Clarify the project's licensing terms in the README

Why:

COPY-PASTE FIX

## License
This project's licensing terms are detailed in the `LICENSE` file. Please refer to that file for specific permissions and limitations.

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 humphd/have-fun-with-machine-learning

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Keras

Recommended in 5 of 2 queries

COMPETITOR LEADERBOARD

Keras · recommended 5×
TensorFlow · recommended 5×
PyTorch · recommended 2×
fastai library · recommended 1×
TensorFlow Hub · recommended 1×

CATEGORY QUERY
Seeking a practical, hands-on guide for beginners to learn image classification with neural networks.
you: not recommended
AI recommended (in order):
1. Keras
2. TensorFlow
3. fastai library
4. PyTorch
5. TensorFlow
6. TensorFlow Hub
7. Keras
8. PyTorch
9. Scikit-Learn
10. Keras
11. TensorFlow
12. deeplearning.ai
13. TensorFlow
14. Keras
AI recommended 14 alternatives but never named humphd/have-fun-with-machine-learning. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I build a simple image classifier using convolutional neural networks as a developer?
you: not recommended
AI recommended (in order):
1. Keras
2. PyTorch Lightning
3. TensorFlow
4. fastai
5. scikit-learn
AI recommended 5 alternatives but never named humphd/have-fun-with-machine-learning. 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 humphd/have-fun-with-machine-learning?
pass
AI did not name humphd/have-fun-with-machine-learning — 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 humphd/have-fun-with-machine-learning in production, what risks or prerequisites should they evaluate first?
pass
AI named humphd/have-fun-with-machine-learning 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 humphd/have-fun-with-machine-learning solve, and who is the primary audience?
pass
AI did not name humphd/have-fun-with-machine-learning — 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)

[![RepoGEO](https://repogeo.com/badge/humphd/have-fun-with-machine-learning.svg)](https://repogeo.com/en/r/humphd/have-fun-with-machine-learning)

HTML

<a href="https://repogeo.com/en/r/humphd/have-fun-with-machine-learning"><img src="https://repogeo.com/badge/humphd/have-fun-with-machine-learning.svg" alt="RepoGEO" /></a>

Pro

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humphd/have-fun-with-machine-learning — 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