RRepoGEO

REPOGEO REPORT · LITE

ThinamXx/300Days__MachineLearningDeepLearning

Default branch main · commit 21c94ba6 · scanned 5/29/2026, 9:33:17 AM

GitHub: 583 stars · 169 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 ThinamXx/300Days__MachineLearningDeepLearning, 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
    Clarify README's opening statement to position as a learning journey

    Why:

    CURRENT
    The README immediately starts with "# **Journey of 300DaysOfData in Machine Learning and Deep Learning**" followed by tables.
    COPY-PASTE FIX
    Add a clear introductory sentence after the H1: "This repository documents my personal 300-day learning journey in Machine Learning and Deep Learning, featuring completed books, research papers, and practical project implementations."
  • mediumhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Set the homepage URL to `https://github.com/ThinamXx/300Days__MachineLearningDeepLearning`
  • lowtopics#3
    Add more specific topics to reflect the learning journey aspect

    Why:

    CURRENT
    deep-learning, machine-learning, python
    COPY-PASTE FIX
    deep-learning, machine-learning, python, learning-path, data-science-journey, project-based-learning, ml-projects

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 ThinamXx/300Days__MachineLearningDeepLearning
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/tensorflow · recommended 1×
  2. fastai/fastai · recommended 1×
  3. pytorch/pytorch · recommended 1×
  4. Microsoft Azure · recommended 1×
  5. pandas-dev/pandas · recommended 1×
  • CATEGORY QUERY
    Where can I find a structured learning path for machine learning and deep learning concepts?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow (tensorflow/tensorflow)
    2. fastai library (fastai/fastai)
    3. PyTorch (pytorch/pytorch)
    4. Microsoft Azure
    5. Pandas (pandas-dev/pandas)
    6. Keras (keras-team/keras)

    AI recommended 6 alternatives but never named ThinamXx/300Days__MachineLearningDeepLearning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Show me practical project examples for implementing deep learning algorithms from scratch.
    you: not recommended
    AI recommended (in order):
    1. TensorFlow
    2. PyTorch

    AI recommended 2 alternatives but never named ThinamXx/300Days__MachineLearningDeepLearning. 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 ThinamXx/300Days__MachineLearningDeepLearning?
    pass
    AI named ThinamXx/300Days__MachineLearningDeepLearning explicitly

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

  • If a team adopts ThinamXx/300Days__MachineLearningDeepLearning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ThinamXx/300Days__MachineLearningDeepLearning 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 ThinamXx/300Days__MachineLearningDeepLearning solve, and who is the primary audience?
    pass
    AI did not name ThinamXx/300Days__MachineLearningDeepLearning — 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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ThinamXx/300Days__MachineLearningDeepLearning — 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