Machine Learning with Python
前往频道在 Telegram
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
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📈 Telegram 频道 Machine Learning with Python 的分析概览
频道 Machine Learning with Python (@codeprogrammer) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 68 482 名订阅者，在 教育 类别中位列第 2 406，并在 印度 地区排名第 4 586 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 68 482 名订阅者。
根据 04 十一月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -75，过去 24 小时变化为 7，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 3.38%。内容发布后 24 小时内通常能获得 1.57% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 2 314 次浏览，首日通常累积 1 073 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 9。
- 主题关注点： 内容集中在 insidead, learning, degree, evaluation, algorithm 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
凭借高频更新（最新数据采集于 05 十一月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 教育 类别中的关键影响点。
68 482
订阅者
+724 小时
-597 天
-7530 天
帖子存档
The Transformer Model
https://machinelearningmastery.com/the-transformer-model/
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How to Add an Editor to Your Django App (Ckeditor)
In this tutorial, we will be looking at how to add an editor to your Django app which will ease your content creation process.
https://bytexplain.com/how-to-add-an-editor-to-your-django-app-ckeditor/?fbclid=IwAR16NutW1rXO6tbtERpnOD4wsseDJlMAR0IZJAE8ivOoms8wMIiUHmexDZk
Public APIs
A collective list of free APIs
📃 Creator: public-apis
📃 Forked by: 27.8k
🌐 GitHub repo: https://github.com/public-apis/public-apis
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pandas for Data Science
Learning Path
Skills: pandas, Data Science, Data Visualization
In this learning path, you’ll get started with pandas and get to know the ins and outs of how you can use it to analyze data with Python.
https://realpython.com/learning-paths/pandas-data-science/
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Automatically find issues in image datasets and practice data-centric computer vision.
CleanVision automatically detects potential issues in image datasets like images that are: blurry, under/over-exposed, (near) duplicates, etc. This data-centric AI package is a quick first step for any computer vision project to find problems in the dataset, which you want to address before applying machine learning. CleanVision is super simple -- run the same couple lines of Python code to audit any image dataset!
https://github.com/cleanlab/cleanvision
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Building Transformer Models with Attention Crash Course. Build a Neural Machine Translator in 12 Days
https://machinelearningmastery.com/building-transformer-models-with-attention-crash-course-build-a-neural-machine-translator-in-12-days/
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Repost from Machine Learning with Python
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Best Data Science Channels and groups on Telegram:
https://t.me/addlist/8_rRW2scgfRhOTc0
Only click on OK and Will automatically add you to all channels
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