RRepoGEO

REPOGEO REPORT · LITE

mikeroyal/Machine-Learning-Guide

Default branch main · commit 20cec9bf · scanned 6/9/2026, 7:32:59 PM

GitHub: 696 stars · 65 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
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 mikeroyal/Machine-Learning-Guide, 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 the README's opening statement to emphasize it's a curated guide

    Why:

    CURRENT
    A guide covering Machine Learning including the applications, libraries and tools that will make you better and more efficient with Machine Learning development.
    COPY-PASTE FIX
    This repository is a comprehensive, curated guide to Machine Learning, covering essential tools, libraries, frameworks, Large Language Models (LLMs), and training models. It's designed to help developers learn and become more efficient with ML development.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root of the repository with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add the repository's GitHub URL (`https://github.com/mikeroyal/Machine-Learning-Guide`) as the homepage URL in the 'About' section.

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 mikeroyal/Machine-Learning-Guide
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Octave
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Octave · recommended 1×
  2. MATLAB · recommended 1×
  3. fastai/fastai · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. tensorflow/tensorflow · recommended 1×
  • CATEGORY QUERY
    What resources are available for learning machine learning development and its core frameworks?
    you: not recommended
    AI recommended (in order):
    1. Octave
    2. MATLAB
    3. fastai (fastai/fastai)
    4. PyTorch (pytorch/pytorch)
    5. TensorFlow (tensorflow/tensorflow)
    6. Keras (keras-team/keras)
    7. Scikit-learn (scikit-learn/scikit-learn)
    8. Kaggle Learn
    9. Pandas (pandas-dev/pandas)
    10. XGBoost (dmlc/xgboost)

    AI recommended 10 alternatives but never named mikeroyal/Machine-Learning-Guide. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I find tools and frameworks for training and deploying large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. DeepSpeed
    4. vLLM
    5. Triton Inference Server
    6. Kubernetes
    7. Kubeflow
    8. OpenAI API
    9. Azure OpenAI Service
    10. Google Cloud Vertex AI

    AI recommended 10 alternatives but never named mikeroyal/Machine-Learning-Guide. 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 mikeroyal/Machine-Learning-Guide?
    pass
    AI did not name mikeroyal/Machine-Learning-Guide — 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 mikeroyal/Machine-Learning-Guide in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mikeroyal/Machine-Learning-Guide 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 mikeroyal/Machine-Learning-Guide solve, and who is the primary audience?
    pass
    AI did not name mikeroyal/Machine-Learning-Guide — 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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mikeroyal/Machine-Learning-Guide — 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