RRepoGEO

REPOGEO REPORT · LITE

armankhondker/awesome-ai-ml-resources

Default branch main · commit 97d34a79 · scanned 5/28/2026, 9:50:06 AM

GitHub: 4,390 stars · 507 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 armankhondker/awesome-ai-ml-resources, 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 clearly state it's a curated resource list

    Why:

    CURRENT
    This repository contains free resources and a roadmap to learn Machine Learning and Artificial Intelligence in 2025.
    COPY-PASTE FIX
    This is an awesome list and curated repository of free resources and a comprehensive roadmap to learn Machine Learning and Artificial Intelligence in 2025.
  • hightopics#2
    Correct topic spelling and add more descriptive topics

    Why:

    CURRENT
    artifical-intelligense, machine-learning, roadmap
    COPY-PASTE FIX
    artificial-intelligence, machine-learning, ai-ml-roadmap, learning-path, free-resources, awesome-list, curated-list
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/armankhondker/awesome-ai-ml-resources

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 armankhondker/awesome-ai-ml-resources
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. numpy/numpy · recommended 2×
  4. pandas-dev/pandas · recommended 2×
  5. scikit-learn/scikit-learn · recommended 2×
  • CATEGORY QUERY
    I'm new to AI/ML, what's a good learning path with free resources?
    you: not recommended
    AI recommended (in order):
    1. Python (python/cpython)
    2. Codecademy
    3. freeCodeCamp (freeCodeCamp/freeCodeCamp)
    4. Google's Python Class
    5. Khan Academy
    6. 3Blue1Brown
    7. Andrew Ng's Machine Learning Course
    8. Coursera
    9. Octave (gnu-octave/octave)
    10. MATLAB
    11. Google's Machine Learning Crash Course
    12. TensorFlow (tensorflow/tensorflow)
    13. fast.ai (fastai/fastai)
    14. PyTorch (pytorch/pytorch)
    15. NumPy (numpy/numpy)
    16. Pandas (pandas-dev/pandas)
    17. Kaggle Learn
    18. Scikit-learn (scikit-learn/scikit-learn)
    19. Keras (keras-team/keras)
    20. Kaggle
    21. Reddit
    22. Discord
    23. Towards Data Science
    24. Analytics Vidhya
    25. Medium

    AI recommended 25 alternatives but never named armankhondker/awesome-ai-ml-resources. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the essential building blocks and concepts for a career in AI/ML?
    you: not recommended
    AI recommended (in order):
    1. Python
    2. NumPy (numpy/numpy)
    3. Pandas (pandas-dev/pandas)
    4. XGBoost (dmlc/xgboost)
    5. LightGBM (microsoft/LightGBM)
    6. BERT
    7. GPT
    8. Scikit-learn (scikit-learn/scikit-learn)
    9. TensorFlow (tensorflow/tensorflow)
    10. Keras (keras-team/keras)
    11. PyTorch (pytorch/pytorch)
    12. Matplotlib (matplotlib/matplotlib)
    13. Seaborn (mwaskom/seaborn)
    14. SQL
    15. AWS
    16. SageMaker
    17. EC2
    18. S3
    19. Google Cloud Platform
    20. AI Platform
    21. Compute Engine
    22. Cloud Storage
    23. Microsoft Azure
    24. Azure Machine Learning
    25. Virtual Machines
    26. Blob Storage
    27. Git
    28. GitHub

    AI recommended 28 alternatives but never named armankhondker/awesome-ai-ml-resources. 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 armankhondker/awesome-ai-ml-resources?
    skipped
    AI did not name armankhondker/awesome-ai-ml-resources — 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 armankhondker/awesome-ai-ml-resources in production, what risks or prerequisites should they evaluate first?
    pass
    AI named armankhondker/awesome-ai-ml-resources 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 armankhondker/awesome-ai-ml-resources solve, and who is the primary audience?
    pass
    AI did not name armankhondker/awesome-ai-ml-resources — 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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