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

Network:Machine Learning with Python India9 949 Technologies & Applications3 403...

📈 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 39 469 subscribers, ranking 3 403 in the Technologies & Applications category and 9 949 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 39 469 subscribers.

According to the latest data from 04 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 356 over the last 30 days and by 7 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 1.27%. Within the first 24 hours after publication, content typically collects 1.09% reactions from the total number of subscribers.
Post reach: On average, each post receives 502 views. Within the first day, a publication typically gains 430 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 05 July, 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.

39 469

Subscribers

+724 hours

+737 days

+35630 days

502

Post views

~ 43024 hours

~ 59948 hours

1.27%

Engagement rate

~ 4

Posts per day

Ads index

beta

Posts Archive

39 476

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

39 476

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

39 476

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

39 476

Python Testing with Selenium Invite your friends 🌹🌹 @DataScience_Books

39 476

Python Testing with Selenium 👇👇👇👇👇

39 476

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

39 476

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

39 476

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

39 476

Healthcare Analytics Made Simple.pdf5.85 MB

39 476

Healthcare Analytics Made Simple Invite your friends 🌹🌹 @DataScience_Books

39 476

Healthcare Analytics Made Simple 👇👇👇👇👇

39 476

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

39 476

Applied Computational Thinking with Python 👇👇👇👇👇

39 476

Applied Computational Thinking with Python 👇👇👇👇👇

39 476

🚀 TensorFlow and PyTorch performance benchmarking in 2021 Habr: https://habr.com/ru/company/ru_mts/blog/565456/ Github: https://github.com/Chifffa/tf_vs_torch_benchmarking Invite your friends 🌹🌹 @DataScience_Books

39 476

🔝 Learning Hierarchical Graph Neural Networks for Image Clustering Github: https://github.com/dmlc/dgl/tree/master/examples/pytorch/hilander Paper: https://arxiv.org/abs/2107.01319 Datasets: https://drive.google.com/file/d/1KLa3uu9ndaCc7YjnSVRLHpcJVMSz868v/view Invite your friends 🌹🌹 @DataScience_Books

39 476

🔎 Microsoft AutoML - Neural Architecture Search New one-shot architecture search framework dedicated to vision transformer search Github: https://github.com/microsoft/AutoML Paper: https://arxiv.org/abs/2107.00651v1 Models: https://drive.google.com/drive/folders/1NLGAbBF9bA1IUAxKlk2VjgRXhr6RHvRW Dataset: https://paperswithcode.com/dataset/cifar-10 Invite your friends 🌹🌹 @DataScience_Books

39 476

Angular and Machine Learning. Invite your friends 🌹🌹 @DataScience_Books

39 476

Angular and Machine Learning. 👇👇👇👇👇

39 476

Deep Learning with JavaScript. Neural Networks in TensorFrow.js Invite your friends 🌹🌹 @DataScience_Books