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Learn Python Coding

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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

Network: Machine Learning with PythonIndia9 131Technologies & Applications3 309...

📈 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 40 902 subscribers, ranking 3 309 in the Technologies & Applications category and 9 131 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 902 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.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“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”

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.

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40 902

Subscribers

+824 hours

+57 days

+8830 days

763

Post views

~ 36224 hours

~ 45648 hours

1.87%

Engagement rate

~ 1

Posts per day

Ads index

Posts Archive

40 902

Python cheat sheet Invite your friends 🌹🌹 @DataScience_Books

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Machine learning book Python Invite your friends 🌹🌹 @DataScience_Books

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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

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

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

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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

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Python Testing with Selenium Invite your friends 🌹🌹 @DataScience_Books

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Python Testing with Selenium 👇👇👇👇👇

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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

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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

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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

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Healthcare Analytics Made Simple.pdf5.85 MB

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Healthcare Analytics Made Simple Invite your friends 🌹🌹 @DataScience_Books

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

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Applied Computational Thinking with Python 👇👇👇👇👇