yanshengjia/ml-road

• highreadme#1

  Strengthen README's opening value proposition

  原因：

  当前

  The README's initial description is concise, followed by a sponsor section.

  复制粘贴的修复

  复制

  Immediately after the H1 and initial description, add a paragraph that explicitly states the repository's purpose as a 'comprehensive, structured learning roadmap' and a 'curated collection of practical resources' for ML, DL, NLP, CV, and Agentic AI, before any sponsor or disclaimer content. For example:
  ```
  # Machine Learning Road
  Machine Learning and Agentic AI Resources, Practice and Research.
  
  This repository provides a comprehensive, structured learning roadmap and a curated collection of practical resources for mastering Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, and the emerging field of Agentic AI. It's designed to guide learners through key concepts, practical implementations, and research trends across these domains, offering a clear path for beginners and intermediate learners.
  ```

  待办完成忽略
• mediumabout#2

  Add homepage URL to repository metadata

  原因：

  当前

  Homepage: (none)

  复制粘贴的修复

  复制

  Set the repository's homepage URL to `https://github.com/yanshengjia/ml-road`.

  待办完成忽略
• mediumreadme#3

  Add explicit scope clarification to README

  原因：

  当前

  The README does not explicitly state that it is not a framework or library.

  复制粘贴的修复

  复制

  Add a sentence or short paragraph to the introductory section of the README (after the core value proposition) clarifying that `ml-road` is a learning resource and roadmap, not a deployable software system, framework, or library. For example, add:
  ```
  Please note: This repository is a curated educational resource and learning roadmap, not a deployable software system, framework, or library for direct production use.
  ```

  待办完成忽略

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

• 品类问题

  Where can I find a curated roadmap for learning machine learning and agentic AI?

  你：未被推荐
  AI 推荐顺序：

  1. OpenAI
  2. DeepLearning.AI
  3. LangChain
  4. LlamaIndex
  5. Hugging Face Transformers Library
  6. fast.ai
  7. Andrew Ng's Machine Learning Specialization

  AI 推荐了 7 个替代方案，却始终没点名 yanshengjia/ml-road。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  What are the best practical resources for deep learning, NLP, and computer vision?

  你：未被推荐
  AI 推荐顺序：

  1. fast.ai courses (fastai/fastai)
  2. PyTorch (pytorch/pytorch)
  3. Hugging Face Transformers Library (huggingface/transformers)
  4. TensorFlow (tensorflow/tensorflow)
  5. Kaggle
  6. Deep Learning Specialization by Andrew Ng
  7. Deep Learning with Python

  AI 推荐了 7 个替代方案，却始终没点名 yanshengjia/ml-road。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of yanshengjia/ml-road?

  pass
  ✓AI 明确点名了 yanshengjia/ml-road

  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 yanshengjia/ml-road in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 yanshengjia/ml-road

  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 yanshengjia/ml-road solve, and who is the primary audience?

  pass
  ✓AI 明确点名了 yanshengjia/ml-road

  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？