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Machine Learning

Machine Learning

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Real Machine Learning β€” simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

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πŸ“ˆ Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 151 subscribers, ranking 3 380 in the Technologies & Applications category and 228 in the Syria 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 40 151 subscribers.

According to the latest data from 29 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 380 over the last 30 days and by 3 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.08%. Within the first 24 hours after publication, content typically collects 1.91% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 837 views. Within the first day, a publication typically gains 766 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
  • Thematic interests: Content is focused on key topics such as distance, insidead, gpu, learning, degree.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œReal Machine Learning β€” simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 30 June, 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.

40 151
Subscribers
+324 hours
+1157 days
+38030 days
Posts Archive
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πŸ“Œ Towards Generalization on Graphs: From Invariance to Causality πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-18 | ⏱️ Read time: 19 min read This blog post shares recent papers on out-of-distribution generalization on graph-structured data

πŸ“Œ A Python Engineer’s Introduction to 3D Gaussian Splatting (Part 3) πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-1
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πŸ“Œ Evaluating ChatGPT’s Data Analysis Improvements: Interactive Tables and Charts πŸ—‚ Category: CHATGPT πŸ•’ Date: 2024-07-19 |
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πŸ“Œ Battling Open Book Exams with Open Source LLMs πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-19 | ⏱️ Read time: 10 min read I
πŸ“Œ Battling Open Book Exams with Open Source LLMs πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-19 | ⏱️ Read time: 10 min read In the age where everyone uses ChatGPT for work and school, I am taking advantage…

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πŸ“Œ Three Mind-Blowing Ideas in Physics: The Stationary Action Principle, Lorentz Transformations, and… πŸ—‚ Category: DATA SCIE
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πŸ“Œ Counterfactuals in Language AI πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-07-23 | ⏱️ Read time: 34 min read with ope
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πŸ“Œ Summer Olympic Games Through the Lens of Data πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-23 | ⏱️ Read time: 13 min read Us
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πŸ“Œ From Ephemeral to Persistence with LangChain: Building Long-Term Memory in Chatbots πŸ—‚ Category: ARTIFICIAL INTELLIGENCE οΏ½
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πŸ“Œ Organizations’ Machine Learning Investment Is (or Should Be) Incremental πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-23 | ⏱
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