RRepoGEO

REPOGEO REPORT · LITE

krishnaik06/Complete-RoadMap-To-Learn-AI

Default branch main · commit a273dbc5 · scanned 6/23/2026, 11:57:54 AM

GitHub: 1,460 stars · 416 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
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 krishnaik06/Complete-RoadMap-To-Learn-AI, 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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise 'About' description

    Why:

    COPY-PASTE FIX
    A comprehensive 2025 roadmap for mastering AI, offering three distinct learning paths: Data Science & Classical AI, Generative AI, and Agentic AI.
  • mediumhomepage#2
    Add project homepage URL (replace placeholder)

    Why:

    COPY-PASTE FIX
    https://[REPLACE_WITH_YOUR_PROJECT_HOMEPAGE_URL]

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 krishnaik06/Complete-RoadMap-To-Learn-AI
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Khan Academy
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Khan Academy · recommended 1×
  2. 3Blue1Brown · recommended 1×
  3. Python for Everybody Specialization · recommended 1×
  4. Automate the Boring Stuff with Python · recommended 1×
  5. Machine Learning (Stanford University on Coursera by Andrew Ng) · recommended 1×
  • CATEGORY QUERY
    Where can I find a structured roadmap to learn artificial intelligence in 2025?
    you: not recommended
    AI recommended (in order):
    1. Khan Academy
    2. 3Blue1Brown
    3. Python for Everybody Specialization
    4. Automate the Boring Stuff with Python
    5. Machine Learning (Stanford University on Coursera by Andrew Ng)
    6. Octave
    7. MATLAB
    8. Deep Learning Specialization (deeplearning.ai on Coursera by Andrew Ng)
    9. TensorFlow
    10. Keras
    11. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
    12. Scikit-learn
    13. PyTorch
    14. Fast.ai
    15. Natural Language Processing Specialization (deeplearning.ai on Coursera)
    16. Stanford CS224N
    17. CS231n
    18. Reinforcement Learning Specialization (University of Alberta on Coursera)
    19. David Silver's Reinforcement Learning Course
    20. Kaggle
    21. arXiv
    22. Hugging Face
    23. GitHub

    AI recommended 23 alternatives but never named krishnaik06/Complete-RoadMap-To-Learn-AI. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best learning resources for becoming a generative AI engineer or data scientist?
    you: not recommended
    AI recommended (in order):
    1. DeepLearning.AI's Generative AI with Large Language Models Specialization
    2. Hugging Face Transformers Library (huggingface/transformers)
    3. fast.ai's Practical Deep Learning for Coders
    4. OpenAI API
    5. Generative Deep Learning by David Foster
    6. Papers With Code
    7. Kaggle Competitions

    AI recommended 7 alternatives but never named krishnaik06/Complete-RoadMap-To-Learn-AI. 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 krishnaik06/Complete-RoadMap-To-Learn-AI?
    pass
    AI named krishnaik06/Complete-RoadMap-To-Learn-AI explicitly

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

  • If a team adopts krishnaik06/Complete-RoadMap-To-Learn-AI in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name krishnaik06/Complete-RoadMap-To-Learn-AI — 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?

  • In one sentence, what problem does the repo krishnaik06/Complete-RoadMap-To-Learn-AI solve, and who is the primary audience?
    pass
    AI did not name krishnaik06/Complete-RoadMap-To-Learn-AI — 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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krishnaik06/Complete-RoadMap-To-Learn-AI — 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