Python Courses & Resources
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Master Python with step-by-step courses β from basics to advanced projects and practical applications. Admin: @HusseinSheikho
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Network:π Analytical overview of Telegram channel Python Courses & Resources
Channel Python Courses & Resources (@python53) in the English language segment is an active participant. Currently, the community unites 50 435 subscribers, ranking 3 434 in the Education category and 7 068 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 50 435 subscribers.
According to the latest data from 01 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 336 over the last 30 days and by 10 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.25%. Within the first 24 hours after publication, content typically collects 1.35% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 135 views. Within the first day, a publication typically gains 682 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 consultation, dentist, insidead, waybienad, appointment.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βMaster Python with step-by-step courses β from basics to advanced projects and practical applications.
Admin: @HusseinSheikhoβ
Thanks to the high frequency of updates (latest data received on 02 September, 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 Education category.
50 435
Subscribers
+1024 hours
+597 days
+33630 days
Posts Archive
Repost from Machine Learning with Python
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Your path starts here, programmer
Free Coursera membership
Only first 284 members
Just collect the stars, each Thousand Stars gives you a subscription for three days .. Subscriptions will be given at the end of the month, you can invite friends to boost your stars
Your path starts here, programmer
Free Coursera membership
Only first 2884 members
Just collect the stars, each Thousand Stars gives you a subscription for three days .. Subscriptions will be given at the end of the month, you can invite friends to boost your stars
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Repost from Machine Learning with Python
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Just collect the stars, each Thousand Stars gives you a subscription for three days .. Subscriptions will be given at the end of the month, you can invite friends to boost your stars
Your path starts here, programmer
Free Coursera membership
Only first 284 members
Just collect the stars, each Thousand Stars gives you a subscription for three days .. Subscriptions will be given at the end of the month, you can invite friends to boost your stars
Repost from Machine Learning with Python
βοΈ Did you miss our in-person Gen AI Rewind event in Bangalore? No worries! We're bringing the AI insights directly to your screen.
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2024's GenAI Highlight:
A concise yet comprehensive overview of the major breakthroughs you need to know.
Building GenAI Intuition:
Understand the fundamentals and grasp how GenAI is evolving at breakneck speed.
Crystal Ball Time:
Insights from our panelists' predictions for the AI landscape in the latter half of 2024.
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Repost from Data Science Books
Python for Natural Language Processing (2024 - 3th)
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Football Analysis in Python with Yolov8 & OpenCV
Video: https://youtu.be/yJWAtr3kvPU?si=Ix9t3QiXR3jwbog7
Deep learning web application for football analysis with Streamlit.
Used material links:
Project repo: https://github.com/Hmzbo/Football-Analytics-with-Deep-Learning-and-Computer-Vision
Custom dataset tips: https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results/
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cvat: https://github.com/opencv/cvat
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