RRepoGEO

REPOGEO REPORT · LITE

yanshengjia/ml-road

Default branch master · commit 56b69df4 · scanned 5/9/2026, 12:28:15 PM

GitHub: 4,757 stars · 1,699 forks

AI VISIBILITY SCORE
28 /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
2 / 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 yanshengjia/ml-road, 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 clarify repo type

    Why:

    CURRENT
    # Machine Learning Road
Machine Learning and Agentic AI Resources, Practice and Research.
    COPY-PASTE FIX
    # Machine Learning Road: A Curated Learning Path and Resource Hub for ML & Agentic AI

This repository serves as a comprehensive Machine Learning Roadmap and guide, offering curated resources, practice materials, and research insights for individuals navigating the fields of Machine Learning and Agentic AI. It is not a deployable software library, framework, or application.
  • mediumtopics#2
    Expand topics to emphasize 'learning resource' nature

    Why:

    CURRENT
    agentic-ai, computer-vision, deep-learning, machine-learning, nlp, pytorch, speech-recognition, tensorflow
    COPY-PASTE FIX
    agentic-ai, computer-vision, deep-learning, machine-learning, nlp, pytorch, speech-recognition, tensorflow, learning-path, education, roadmap, curated-resources, ml-resources, ai-resources
  • lowhomepage#3
    Add a homepage link to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/yanshengjia

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 yanshengjia/ml-road
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
tensorflow/tensorflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/tensorflow · recommended 2×
  2. pytorch/pytorch · recommended 2×
  3. huggingface/transformers · recommended 2×
  4. Khan Academy · recommended 1×
  5. MIT OpenCourseware · recommended 1×
  • CATEGORY QUERY
    Seeking a structured roadmap for mastering machine learning and related AI topics.
    you: not recommended
    AI recommended (in order):
    1. Khan Academy
    2. MIT OpenCourseware
    3. DataCamp
    4. Coursera
    5. NumPy (numpy/numpy)
    6. Pandas (pandas-dev/pandas)
    7. Matplotlib (matplotlib/matplotlib)
    8. Seaborn (mwaskom/seaborn)
    9. Scikit-learn (scikit-learn/scikit-learn)
    10. Keras (keras-team/keras)
    11. TensorFlow (tensorflow/tensorflow)
    12. DeepLearning.AI
    13. fast.ai (fastai/fastai)
    14. PyTorch (pytorch/pytorch)
    15. Hugging Face
    16. Hugging Face Transformers (huggingface/transformers)
    17. NLTK (nltk/nltk)
    18. spaCy (explosion/spaCy)
    19. OpenCV (opencv/opencv)
    20. PyTorch torchvision (pytorch/vision)
    21. TensorFlow Keras Applications
    22. OpenAI Gym (openai/gym)
    23. Stable Baselines3 (DLR-RM/stable-baselines3)
    24. OpenAI API
    25. ChatGPT
    26. GPT-3/4
    27. Google AI Studio
    28. Gemini
    29. Hugging Face Hub
    30. Kaggle
    31. Google Colaboratory
    32. Flask (pallets/flask)
    33. FastAPI (tiangolo/fastapi)
    34. Streamlit (streamlit/streamlit)
    35. Docker
    36. AWS SageMaker
    37. Google Cloud AI Platform

    AI recommended 37 alternatives but never named yanshengjia/ml-road. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are comprehensive learning resources for computer vision, NLP, and deep learning?
    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. Stanford University's CS231n: Convolutional Neural Networks for Visual Recognition
    4. Stanford University's CS224n: Natural Language Processing with Deep Learning
    5. 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
    6. Hugging Face Transformers (huggingface/transformers)
    7. PyTorch (pytorch/pytorch)
    8. TensorFlow (tensorflow/tensorflow)

    AI recommended 8 alternatives but never named yanshengjia/ml-road. 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 yanshengjia/ml-road?
    pass
    AI named yanshengjia/ml-road explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts yanshengjia/ml-road in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yanshengjia/ml-road 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 yanshengjia/ml-road solve, and who is the primary audience?
    pass
    AI did not name yanshengjia/ml-road — 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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  • Brand-free category queries5 vs 2 in Lite
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