RRepoGEO

REPOGEO REPORT · LITE

llSourcell/Machine_Learning_Journey

Default branch master · commit 02f66ba8 · scanned 6/8/2026, 12:18:14 AM

GitHub: 960 stars · 246 forks

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 llSourcell/Machine_Learning_Journey, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    machine-learning, ml-curriculum, deep-learning, data-science, education, youtube, siraj-raval, portfolio-building, aws, google-cloud, azure, kaggle
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the repository root to clearly state the terms of use.
  • mediumhomepage#3
    Add the YouTube series URL as the repository homepage

    Why:

    COPY-PASTE FIX
    Add the direct URL to the 'Machine Learning Journey' YouTube playlist or introductory video as the repository homepage.

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 llSourcell/Machine_Learning_Journey
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Andrew Ng's Machine Learning Specialization
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Andrew Ng's Machine Learning Specialization · recommended 1×
  2. DeepLearning.AI TensorFlow in Practice Specialization · recommended 1×
  3. fast.ai's Practical Deep Learning for Coders · recommended 1×
  4. Google's Machine Learning Crash Course · recommended 1×
  5. MIT OpenCourseWare - Introduction to Deep Learning (6.S191) · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive machine learning curriculum to guide my learning journey?
    you: not recommended
    AI recommended (in order):
    1. Andrew Ng's Machine Learning Specialization
    2. DeepLearning.AI TensorFlow in Practice Specialization
    3. fast.ai's Practical Deep Learning for Coders
    4. Google's Machine Learning Crash Course
    5. MIT OpenCourseWare - Introduction to Deep Learning (6.S191)
    6. Stanford University's CS229: Machine Learning

    AI recommended 6 alternatives but never named llSourcell/Machine_Learning_Journey. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best resources for building a machine learning portfolio and understanding industry practices?
    you: not recommended
    AI recommended (in order):
    1. Kaggle
    2. Towards Data Science
    3. Google Cloud Skills Boost
    4. fast.ai Practical Deep Learning for Coders
    5. GitHub
    6. Hugging Face
    7. DataCamp
    8. StrataScratch

    AI recommended 8 alternatives but never named llSourcell/Machine_Learning_Journey. 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 llSourcell/Machine_Learning_Journey?
    pass
    AI did not name llSourcell/Machine_Learning_Journey — 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 llSourcell/Machine_Learning_Journey in production, what risks or prerequisites should they evaluate first?
    pass
    AI named llSourcell/Machine_Learning_Journey 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 llSourcell/Machine_Learning_Journey solve, and who is the primary audience?
    pass
    AI did not name llSourcell/Machine_Learning_Journey — 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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llSourcell/Machine_Learning_Journey — 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