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alirezadir/Machine-Learning-Interviews

默认分支 main · commit 164d43a8 · 扫描时间 2026/5/11 03:27:34

星标 8,158 · Fork 1,461

AI 可见性总分
22 /100
亟需修复
品类召回
0 / 2
在所有问题中均未被推荐
规则结果
通过 1 · 警告 1 · 失败 0
客观元数据检查
AI 认识你的名字
1 / 3
直接询问时，AI 是否点名你的仓库
如何阅读这份报告

行动计划告诉你下一步要做什么——按影响力排序、可直接复制粘贴的修改。品类可见性是真正的 GEO 测试：当用户向 AI 提一个不带品牌、本应让 alirezadir/Machine-Learning-Interviews 浮出水面的问题时，AI 是真的推荐了你，还是推荐了你的竞品？客观检查验证 AI 引擎最先权衡的那些元数据信号。自指检查判断 AI 是否还认识你的名字。

行动计划 — 可复制粘贴的修复

3 条由 gemini-2.5-flash 生成、按优先级排序的修改。修完后请把对应条目标记为完成。

整体方向
  • highreadme#1
    Reposition the 'News' section in the README

    原因：

    当前
    The 'News' section appears directly after the H1.
    复制粘贴的修复
    Move the 'News' section to appear *after* the main introductory paragraph that describes this repository's purpose (i.e., after 'This repo aims to serve as a guide to prepare for Machine Learning (AI) Engineering interviews...').
  • highhomepage#2
    Add a homepage URL to the repository's About section

    原因：

    复制粘贴的修复
    https://alirezadir.com/ml-interviews (or similar relevant URL if a dedicated page exists)
  • mediumreadme#3
    Strengthen the README's opening to highlight unique value proposition

    原因：

    当前
    This repo aims to serve as a guide to prepare for **Machine Learning (AI) Engineering** interviews for relevant roles at big tech companies (in particular FAANG). It has compiled based on the author's personal experience and notes from his own interview preparation, when he received offers from Meta (ML Specialist), Google (ML Engineer), Amazon (Applied Scientist), Apple (Applied Scientist), and Rok
    复制粘贴的修复
    This repository is the definitive, experience-driven guide for **Machine Learning (AI) Engineering** technical interviews, specifically designed to help candidates secure roles at top-tier tech companies like FAANG. Unlike general ML learning resources, this guide focuses exclusively on the practical, frequently asked questions and system design challenges encountered in real-world interviews, compiled from the author's successful interview preparation leading to offers from Meta, Google, Amazon, and Apple.

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

品类可见性 — 真正的 GEO 测试

向 google/gemini-2.5-flash 提出的不带品牌问题。AI 推荐了你，还是推荐了别人？

各模型使用同一组问题 — 切换标签对比回答与排名。

召回
0 / 2
0% 的问题里出现了 alirezadir/Machine-Learning-Interviews
平均排名
—
越小越好。#1 表示首位推荐。
声量占比
0%
在所有被点名的工具中，你占了多少？
头号对手
Machine Learning System Design Interview
在 2 个问题中被推荐 2 次
竞品排行
  1. Machine Learning System Design Interview · 被推荐 2 次
  2. Machine Learning Yearning · 被推荐 1 次
  3. An Introduction to Statistical Learning with Applications in R · 被推荐 1 次
  4. The Elements of Statistical Learning · 被推荐 1 次
  5. Coursera: Machine Learning by Andrew Ng · 被推荐 1 次
  • 品类问题
    Seeking a comprehensive guide to prepare for machine learning and AI engineering technical interviews.
    你：未被推荐
    AI 推荐顺序：
    1. Machine Learning Yearning
    2. An Introduction to Statistical Learning with Applications in R
    3. The Elements of Statistical Learning
    4. Coursera: Machine Learning by Andrew Ng
    5. Deep Learning Specialization
    6. Machine Learning Engineering for Production (MLOps) Specialization
    7. PyTorch
    8. TensorFlow/Keras
    9. scikit-learn
    10. LeetCode
    11. Cracking the Coding Interview by Gayle Laakmann McDowell
    12. Designing Data-Intensive Applications by Martin Kleppmann
    13. Machine Learning System Design Interview
    14. Grokking the System Design Interview
    15. Probability and Statistics for Engineers and Scientists by Walpole, Myers, et al.
    16. Khan Academy: Statistics and Probability
    17. STAR Method
    18. Cracking the PM Interview by Gayle Laakmann McDowell and Jackie Bavaro
    19. Pramp.com
    20. Interviewing.io
    21. arXiv.org
    22. Papers With Code
    23. Twitter
    24. Google AI Blog
    25. OpenAI Blog
    26. Meta AI Blog

    AI 推荐了 26 个替代方案，却始终没点名 alirezadir/Machine-Learning-Interviews。这就是要补上的差距。

    查看 AI 完整回答
  • 品类问题
    Where can I find resources for AI agentic systems and scalable AI engineering interview preparation?
    你：未被推荐
    AI 推荐顺序：
    1. LangChain
    2. LlamaIndex
    3. Designing Data-Intensive Applications
    4. Hugging Face Transformers
    5. Accelerate
    6. Machine Learning System Design Interview
    7. ByteByteGo
    8. OpenAI API
    9. Assistants API

    AI 推荐了 9 个替代方案，却始终没点名 alirezadir/Machine-Learning-Interviews。这就是要补上的差距。

    查看 AI 完整回答

客观检查

针对 AI 引擎最看重的元数据信号的规则审计。

  • Metadata completeness
    warn

    建议：

  • README presence
    pass

自指检查

当被直接问到你时，AI 是否还知道你的仓库存在？

  • Compared to common alternatives in this category, what is the core differentiator of alirezadir/Machine-Learning-Interviews?
    pass
    AI 未点名 alirezadir/Machine-Learning-Interviews —— 很可能在说另一个项目

    AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

  • If a team adopts alirezadir/Machine-Learning-Interviews in production, what risks or prerequisites should they evaluate first?
    pass
    AI 明确点名了 alirezadir/Machine-Learning-Interviews

    AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

  • In one sentence, what problem does the repo alirezadir/Machine-Learning-Interviews solve, and who is the primary audience?
    pass
    AI 未点名 alirezadir/Machine-Learning-Interviews —— 很可能在说另一个项目

    AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

嵌入你的 GEO 徽章

把这个徽章贴进 alirezadir/Machine-Learning-Interviews 的 README。每次重新扫描都会自动更新，并跳到最新报告——是「我在乎 AI 可发现性」最简单的公开证明。

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Pro

订阅 Pro，解锁深度诊断

alirezadir/Machine-Learning-Interviews — 轻量扫描仍免费；本卡列出 Pro 相对轻量的深度额度。

  • 深度报告每月 10 次
  • 无品牌品类查询5，轻量 2
  • 优先行动项8，轻量 3