不要被骗子欺骗！Telemetrio 会找到并标记这些频道 👉 如果想查看标记，请订阅

Learn Python Coding

前往频道在 Telegram

Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills. Admin: @HusseinSheikho || @Hussein_Sheikho

显示更多

网络: Machine Learning with Python印度9 131技术与应用3 309...

📈 Telegram 频道 Learn Python Coding 的分析概览

频道 Learn Python Coding (@pythonre) 英语语言赛道中的是活跃参与者。目前社区聚集了 40 902 名订阅者，在 技术与应用 类别中位列第 3 309，并在印度地区排名第 9 131 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 40 902 名订阅者。

根据 12 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 88，过去 24 小时变化为 8，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 1.87%。内容发布后 24 小时内通常能获得 0.89% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 763 次浏览，首日通常累积 362 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 2。
主题关注点： 内容集中在 math, harvard, oxford, supervision, waybienad 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 Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills. Admin: @HusseinSheikho || @Hussein_Sheikho”

凭借高频更新（最新数据采集于 13 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 技术与应用 类别中的关键影响点。

Buy Ad

40 902

订阅者

+824 小时

+57 天

+8830 天

763

帖子浏览量

~ 36224 小时

~ 45648 小时

1.87%

参与率

~ 1

每日帖子数

Ads index

帖子存档

40 902

Python cheat sheet Invite your friends 🌹🌹 @DataScience_Books

40 902

Python cheat sheet 👇👇👇👇👇

40 902

Machine learning book Python Invite your friends 🌹🌹 @DataScience_Books

40 902

Machine learning book Python 👇👇👇👇👇

40 902

RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds (CVPR 2020) Github: https://github.com/QingyongHu/RandLA-Net Paper: https://arxiv.org/abs/1911.11236 Project: http://randla-net.cs.ox.ac.uk/ Invite your friends 🌹🌹 @DataScience_Books

40 902

The Big Book of Small Python Invite your friends 🌹🌹 @DataScience_Books

40 902

The Big Book of Small Python 👇👇👇👇👇

40 902

R and Python for Oceanographers: A Practical Guide with Applications Invite your friends 🌹🌹 @DataScience_Books

40 902

R and Python for Oceanographers: A Practical Guide with Applications 👇👇👇👇👇

40 902

FEDOT - AutoML framework for composite pipelines FEDOT is an open-source framework for automated modeling and machine learning (AutoML). It can build custom modeling pipelines for different real-world processes in an automated way using an evolutionary approach. FEDOT supports classification (binary and multiclass), regression, clustering, and time series prediction tasks, as well as different data types and multi-modal cases. Also, sensitivity analysis of the pipelines, custom pipelines design as the initial assumption of optimization, domain-specific objective functions, and other interesting features are implemented. Github: https://github.com/nccr-itmo/FEDOT Preprint: https://arxiv.org/abs/2106.15397 Intro: https://www.youtube.com/watch?v=RjbuV6i6de4 Invite your friends 🌹🌹 @DataScience_Books

40 902

Python Testing with Selenium Invite your friends 🌹🌹 @DataScience_Books

40 902

Python Testing with Selenium 👇👇👇👇👇

40 902

Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets Github: https://github.com/HayeonLee/MetaD2A Paper: https://arxiv.org/abs/2107.00860v1 Invite your friends 🌹🌹 @DataScience_Books

40 902

Deep Transfer Learning Baselines for Sentiment Analysis in Russian Github: https://github.com/sismetanin/sentiment-analysis-in-russian Paper: https://www.sciencedirect.com/science/article/abs/pii/S0306457320309730?dgcid=author Invite your friends 🌹🌹 @DataScience_Books

40 902

PlanSys2: A Planning System Framework for ROS2 Github: https://github.com/IntelligentRoboticsLabs/ros2_planning_system Paper: https://arxiv.org/abs/2107.00376v1 Invite your friends 🌹🌹 @DataScience_Books

40 902

Healthcare Analytics Made Simple.pdf5.85 MB

40 902

Healthcare Analytics Made Simple Invite your friends 🌹🌹 @DataScience_Books

40 902

Healthcare Analytics Made Simple 👇👇👇👇👇

40 902

Applied Computational Thinking with Python Invite your friends 🌹🌹 @DataScience_Books

40 902

Applied Computational Thinking with Python 👇👇👇👇👇