Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
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π Analytical overview of Telegram channel Machine Learning with Python
Channel Machine Learning with Python (@codeprogrammer) in the English language segment is an active participant. Currently, the community unites 68 482 subscribers, ranking 2 406 in the Education category and 4 586 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 68 482 subscribers.
According to the latest data from 04 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -75 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 3.38%. Within the first 24 hours after publication, content typically collects 1.57% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 314 views. Within the first day, a publication typically gains 1 073 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 9.
- Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βLearn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikhoβ
Thanks to the high frequency of updates (latest data received on 05 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 Education category.
68 482
Subscribers
+724 hours
-597 days
-7530 days
Posts Archive
Python Cheat Sheet for Beginners
Python is the most popular programming language in data science. Use this cheat sheet to jumpstart your Python learning journey.
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Deep Learning with PyTorch Cheat Sheet
Learn everything you need to know about PyTorch in this convenient cheat sheet
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Before the deep learning revolution redefined computer vision, Haar features and Haar cascades were the tools you must not ignore for object detection. Even today, they are very useful object detectors because they are lightweight. In this post, you will learn about the Haar cascade and how it can detect objects. After completing this post, you will know:
What is Haar features
How Haar cascade is using Haar features to detect objects
Some predefined Haar cascade object detectors in OpenCV
Letβs get started.
