en
Feedback

Don't get caught by a cheater! Telemetrio finds and tags such channels πŸ‘‰ If you want to see the tag, subscribe

Learn Python Coding

Learn Python Coding

Open in 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

Show more

πŸ“ˆ Analytical overview of Telegram channel Learn Python Coding

Channel Learn Python Coding (@pythonre) in the English language segment is an active participant. Currently, the community unites 40 895 subscribers, ranking 3 309 in the Technologies & Applications category and 9 131 in the India region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 40 895 subscribers.

According to the latest data from 12 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 88 over the last 30 days and by 8 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 1.87%. Within the first 24 hours after publication, content typically collects 0.89% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 763 views. Within the first day, a publication typically gains 362 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
  • Thematic interests: Content is focused on key topics such as math, harvard, oxford, supervision, waybienad.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œLearn 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”

Thanks to the high frequency of updates (latest data received on 13 November, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

Buy Ad
40 895
Subscribers
+824 hours
+57 days
+8830 days
Posts Archive
Deep Learning and Physics πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡
Deep Learning and Physics πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡

Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD Invite your friends 🌹🌹 @DataScience_Books

Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡
Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡

Java Cookbook: Problems and Solutions for Java Developers, 4th Edition, final release Invite your friends 🌹🌹 @DataScience_Books

Java Cookbook: Problems and Solutions for Java Developers, 4th Edition, final release πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡
Java Cookbook: Problems and Solutions for Java Developers, 4th Edition, final release πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡

Hands-On SQL Server 2019 Analysis Services Invite your friends 🌹🌹 @DataScience_Books

Hands-On SQL Server 2019 Analysis Services πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡
Hands-On SQL Server 2019 Analysis Services πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡

Data Science Essentials in Python Invite your friends 🌹🌹 @DataScience_Books

Data Science Essentials in Python πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡
Data Science Essentials in Python πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡

AI for Games, 3d edition Invite your friends 🌹🌹 @DataScience_Books

AI for Games, 3d edition πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡
AI for Games, 3d edition πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡

Probabilistic two-stage detection Two-stage object detectors that use class-agnostic one-stage detectors as the proposal network. github: https://github.com/xingyizhou/CenterNet2?utm_source=catalyzex.com paper: https://arxiv.org/pdf/2103.07461.pdf Invite your friends 🌹🌹 @DataScience_Books

A curated list of awesome Python frameworks, libraries, software and resources. github: https://github.com/vinta/awesome-python Invite your friends 🌹🌹 @DataScience_Books

Invite your friends 🌹🌹 @DataScience_Books

Optimal transport in multilayer networks Github: https://github.com/cdebacco/MultiOT Paper: https://arxiv.org/abs/2106.07202v
Optimal transport in multilayer networks Github: https://github.com/cdebacco/MultiOT Paper: https://arxiv.org/abs/2106.07202v1 Invite your friends 🌹🌹 @DataScience_Books

Part-aware Panoptic Segmentation Github: https://github.com/pmeletis/panoptic_parts Paper: https://arxiv.org/abs/2106.06351v1
Part-aware Panoptic Segmentation Github: https://github.com/pmeletis/panoptic_parts Paper: https://arxiv.org/abs/2106.06351v1 Docs: https://panoptic-parts.readthedocs.io/en/stable Invite your friends 🌹🌹 @DataScience_Books

Color2Style: Real-Time Exemplar-Based Image Colorization with Self-Reference Learning and Deep Feature Modulation ArXiV: http
Color2Style: Real-Time Exemplar-Based Image Colorization with Self-Reference Learning and Deep Feature Modulation ArXiV: https://arxiv.org/pdf/2106.08017.pdf Invite your friends 🌹🌹 @DataScience_Books

Python machine learning from scratch Invite your friends 🌹🌹 @DataScience_Books

Python machine learning from scratch πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡
Python machine learning from scratch πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡

βœ… Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs Github: https://github.com/seongjunyun/Graph_Transfor
βœ… Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs Github: https://github.com/seongjunyun/Graph_Transformer_Networks Paper: https://arxiv.org/abs/2106.06218v1 Dataset: https://github.com/Jhy1993/HAN Invite your friends 🌹🌹 @DataScience_Books