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500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

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AI 模型与开发框架教育与学习 artificial-intelligenceartificial-intelligence-projectsawesomecomputer-visioncomputer-vision-projectdata-science
37,732
Stars
7,543
Forks
703
Watchers
71
Open Issues
待补全
许可证
未知
语言
2021-01-02
创建时间
2026-09-26
最近更新

项目能做什么

汇集 500 个附带源码的 AI、机器学习、深度学习、计算机视觉和 NLP 实战项目。
来源:基于仓库元数据 · 内容以仓库实际 README 为准

使用说明 查看原始 README 原仓库文档

中文使用说明暂未生成。下面保留原仓库 README 摘录,命令与配置均以原文为准。
原始 README 摘录(英文原文,节选前 3000 字符)
## 500 + 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗟𝗶𝘀𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗱𝗲

***500 AI Machine learning Deep learning Computer vision NLP Projects with code* !!!**

![](https://raw.githubusercontent.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code/main/images/Colorful%20Futuristic%20Technology%20Poster.gif)

Follow me on LinkedIn : [![](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/ashishpatel2604/)

***This list is continuously updated.*** - You can take pull requests and contribute. All Links are tested and working fine. Please ping if any link doesn't work

| Sr No | Name                                                         | Link                                                         |
| ----- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| 1     | 365 Days Computer Vision Learning                            | [👆](https://github.com/ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post) |
| 2     | 125+ NLP Language Models Treasure of Transformers            | [👆](https://github.com/ashishpatel26/Treasure-of-Transformers) |
| 3     | Andrew NG ML notes                                           | [👆](https://github.com/ashishpatel26/Andrew-NG-Notes)        |
| 4     | 10 Machine Learning Projects on Time Series Forecasting      | [👆](https://medium.com/coders-camp/10-machine-learning-projects-on-time-seri%20es-forecasting-ee0368420ccd) |
| 5     | 20 Deep Learning Projects Solved and Explained with Python   | [👆](https://thecleverprogrammer.com/2020/11/22/deep-learning-projects-with-python/) |
| 6     | 20 Machine learning Project                                  | [👆](https://amankharwal.medium.com/20-machine-learning-projects-for-portfolio-81e3dbd167b1) |
| 7     | 30 Python Project Solved and Explained                       | [👆](https://amankharwal.medium.com/30-python-projects-solved-and-explained-563fd7473003) |
| 8     | Machine learning Course for Free                             | [👆](https://thecleverprogrammer.com/2020/09/24/machine-learning-course/) |
| 9     | 5 Web Scraping Projects with Python                          | [👆](https://amankharwal.medium.com/5-web-scraping-projects-with-python-4bcc25ff039) |
| 10    | 20 Machine Learning Projects on Future Prediction with Python | [👆](https://amankharwal.medium.com/20-machine-learning-projects-on-future-prediction-with-python-93932d9a7f7f) |
| 11    | 4 Chatbot Project With Python                                | [👆](https://amankharwal.medium.com/4-chatbot-projects-with-python-5b32fd84af37) |
| 12    | 7 Python Gui project                                         | [👆](https://amankharwal.medium.com/7-python-gui-projects-for-beginners-87ae2c695d78) |
| 13    | All Unsupervised learning Projects                           | [👆](https://amankharwal.medium.com/all-uns
阅读完整原文

项目深度分析 规则生成(AI 未配置)

综合评分 54.0 分。该项目在“个人可实施性”维度得 35 分,“付费可能性”维度得 20.0 分,差异化空间 100.0 分(评分依据见评分卡)。

面向细分需求的二次开发产品

产品构想基于 500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code 的核心能力,聚焦一个更窄的场景(如某平台、某行业)做差异化。
目标用户该细分场景用户
难度/周期中 · 2~6 周
收费模式买断制 / 订阅制 / 广告
竞争风险细分需求规模需验证
依据 AI 推测:基于项目主题标签的判断
以上为规则引擎基于仓库公开数据生成的参考结论,包含推测成分,不构成投资或创业承诺;配置 AI 后可获得更深入的分析。
报告会保存到 个人中心

竞品与相似项目 GitHub Search 真实检索

相似项目数据正在完善中,暂不展示(避免误导)

项目优势与风险

技术成熟度创建于 2021-01-02,最近推送 2026-09-26
社区活跃Star 37,732 / Fork 7,543 / Issue 71(数据支持,可自行核实)
许可证未检测到(需人工确认)
主要风险上游项目方向可能变化;同赛道竞争者较多(见竞品表);热度与需求不完全等价

安装与使用

此项目未提供 Docker 镜像,安装步骤以原仓库 README 为准(涉及依赖安装与环境配置,对新手门槛较高)。