RRepoGEO

REPOGEO REPORT · LITE

zhengjingwei/machine-learning-interview

Default branch master · commit 51323ebe · scanned 5/20/2026, 2:03:18 AM

GitHub: 1,666 stars · 218 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 zhengjingwei/machine-learning-interview, 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
    Add a clear introductory statement to the README

    Why:

    CURRENT
    [TOC]

# 一、机器学习相关
    COPY-PASTE FIX
    本仓库旨在为算法工程师和机器学习岗位的面试者提供全面的面试题总结与解答，涵盖机器学习、深度学习等核心概念和算法。

[TOC]

# 一、机器学习相关
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root. For example, to allow free use and sharing, add a standard MIT License file.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add a relevant URL to the repository's homepage field in the GitHub settings, such as a personal blog, project page, or a related resource where more context about the interview questions is provided.

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 zhengjingwei/machine-learning-interview
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Towards Data Science
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Towards Data Science · recommended 2×
  2. scikit-learn · recommended 1×
  3. XGBoost · recommended 1×
  4. TensorFlow · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    What are common machine learning interview questions and how to answer them?
    you: not recommended
    AI recommended (in order):
    1. scikit-learn
    2. XGBoost
    3. TensorFlow
    4. PyTorch
    5. LightGBM
    6. CatBoost
    7. OpenAI Gym
    8. Stable Baselines3
    9. SMOTE
    10. imbalanced-learn
    11. Pandas
    12. NumPy
    13. Matplotlib
    14. Seaborn
    15. MLflow
    16. Docker
    17. Surprise
    18. TensorFlow Recommenders
    19. LightFM
    20. Great Expectations
    21. Prometheus
    22. Grafana
    23. Towards Data Science
    24. The Batch
    25. DeepLearning.AI
    26. Google AI Blog
    27. OpenAI Blog
    28. NeurIPS
    29. ICML
    30. KDD
    31. CVPR
    32. ACL
    33. Coursera
    34. edX
    35. fast.ai
    36. arXiv
    37. GitHub
    38. Lex Fridman Podcast
    39. Data Skeptic

    AI recommended 39 alternatives but never named zhengjingwei/machine-learning-interview. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find explanations for fundamental machine learning concepts and problem-solving strategies?
    you: not recommended
    AI recommended (in order):
    1. Coursera's Machine Learning by Andrew Ng
    2. Hands-On Machine Learning with Scikit-Learn, Keras, & TensorFlow (ageron/handson-ml3)
    3. Scikit-Learn (scikit-learn/scikit-learn)
    4. Keras (keras-team/keras)
    5. TensorFlow (tensorflow/tensorflow)
    6. StatQuest with Josh Starmer
    7. The Hundred-Page Machine Learning Book (burkov/the-hundred-page-machine-learning-book)
    8. fast.ai's Practical Deep Learning for Coders (fastai/fastbook)
    9. Towards Data Science

    AI recommended 9 alternatives but never named zhengjingwei/machine-learning-interview. 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 zhengjingwei/machine-learning-interview?
    pass
    AI did not name zhengjingwei/machine-learning-interview — 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 zhengjingwei/machine-learning-interview in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zhengjingwei/machine-learning-interview 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 zhengjingwei/machine-learning-interview solve, and who is the primary audience?
    pass
    AI did not name zhengjingwei/machine-learning-interview — 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

Drop this badge into the README of zhengjingwei/machine-learning-interview. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/zhengjingwei/machine-learning-interview.svg)](https://repogeo.com/en/r/zhengjingwei/machine-learning-interview)
HTML
<a href="https://repogeo.com/en/r/zhengjingwei/machine-learning-interview"><img src="https://repogeo.com/badge/zhengjingwei/machine-learning-interview.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

zhengjingwei/machine-learning-interview — 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