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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 40 072 subscribers, ranking 3 398 in the Technologies & Applications category and 232 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 40 072 subscribers.

According to the latest data from 23 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 379 over the last 30 days and by 30 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 1.92%. Within the first 24 hours after publication, content typically collects 1.16% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 770 views. Within the first day, a publication typically gains 466 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 24 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.

40 072
Subscribers
+3024 hours
+337 days
+37930 days
Posts Archive
πŸ“Œ An End-to-End Guide to Beautifying Your Open-Source Repo with Agentic AI πŸ—‚ Category: LLM APPLICATIONS πŸ•’ Date: 2026-02-20
πŸ“Œ An End-to-End Guide to Beautifying Your Open-Source Repo with Agentic AI πŸ—‚ Category: LLM APPLICATIONS πŸ•’ Date: 2026-02-20 | ⏱️ Read time: 17 min read The guide to automated improvement of scientific and industrial repositories using open-source AI agents #DataScience #AI #Python

πŸ“Œ Donkeys, Not Unicorns πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2026-02-20 | ⏱️ Read time: 8 min read The New Rules of
πŸ“Œ Donkeys, Not Unicorns πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2026-02-20 | ⏱️ Read time: 8 min read The New Rules of Entrepreneurship in the Era of Commoditized Magic #DataScience #AI #Python

πŸ“Œ AI in Multiple GPUs: How GPUs Communicate πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2026-02-19 | ⏱️ Read time: 5 min r
πŸ“Œ AI in Multiple GPUs: How GPUs Communicate πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2026-02-19 | ⏱️ Read time: 5 min read A deep dive into the hardware infrastructure that enables multi-GPU communication for AI workloads #DataScience #AI #Python

πŸ“Œ AlpamayoR1: Large Causal Reasoning Models for Autonomous Driving πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2026-02-19
πŸ“Œ AlpamayoR1: Large Causal Reasoning Models for Autonomous Driving πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2026-02-19 | ⏱️ Read time: 9 min read All you need to know about Chain of Causation reasoning and the current state of… #DataScience #AI #Python

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πŸ“Œ Understanding the Chi-Square Test Beyond the Formula πŸ—‚ Category: STATISTICS πŸ•’ Date: 2026-02-19 | ⏱️ Read time: 17 min re
πŸ“Œ Understanding the Chi-Square Test Beyond the Formula πŸ—‚ Category: STATISTICS πŸ•’ Date: 2026-02-19 | ⏱️ Read time: 17 min read How categorical data becomes statistical evidence. #DataScience #AI #Python

πŸ“Œ The Missing Curriculum: Essential Concepts For Data Scientists in the Age of AI Coding Agents πŸ—‚ Category: PROGRAMMING πŸ•’
πŸ“Œ The Missing Curriculum: Essential Concepts For Data Scientists in the Age of AI Coding Agents πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2026-02-19 | ⏱️ Read time: 13 min read AI can write the code, but you have to steer the ship. Master the knowledge… #DataScience #AI #Python

πŸ“Œ Agentic AI for Modern Deep Learning Experimentation πŸ—‚ Category: AGENTIC AI πŸ•’ Date: 2026-02-18 | ⏱️ Read time: 14 min rea
πŸ“Œ Agentic AI for Modern Deep Learning Experimentation πŸ—‚ Category: AGENTIC AI πŸ•’ Date: 2026-02-18 | ⏱️ Read time: 14 min read Stop babysitting training runs. Start shipping research. Autonomous experiment management built for/by deep learning engineers. #DataScience #AI #Python

πŸ“Œ Why Every Analytics Engineer Needs to Understand Data Architecture πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2026-02-18 | ⏱️
πŸ“Œ Why Every Analytics Engineer Needs to Understand Data Architecture πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2026-02-18 | ⏱️ Read time: 11 min read Get the data architecture right, and everything else becomes easier. I know it sounds simple,… #DataScience #AI #Python

πŸ“Œ Building Cost-Efficient Agentic RAG on Long-Text Documents in SQL Tables πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2026-
πŸ“Œ Building Cost-Efficient Agentic RAG on Long-Text Documents in SQL Tables πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2026-02-18 | ⏱️ Read time: 13 min read Designing a hybrid SQL + vector retrieval system without schema changes, data migration, or performance… #DataScience #AI #Python

πŸ“Œ Can AI Solve Failures in Your Supply Chain? πŸ—‚ Category: AGENTIC AI πŸ•’ Date: 2026-02-18 | ⏱️ Read time: 16 min read When y
πŸ“Œ Can AI Solve Failures in Your Supply Chain? πŸ—‚ Category: AGENTIC AI πŸ•’ Date: 2026-02-18 | ⏱️ Read time: 16 min read When your warehouse and transportation teams blame each other for late deliveries, who’s right? We… #DataScience #AI #Python

πŸ“Œ Use OpenClaw to Make a Personal AI Assistant πŸ—‚ Category: AGENTIC AI πŸ•’ Date: 2026-02-17 | ⏱️ Read time: 10 min read Learn
πŸ“Œ Use OpenClaw to Make a Personal AI Assistant πŸ—‚ Category: AGENTIC AI πŸ•’ Date: 2026-02-17 | ⏱️ Read time: 10 min read Learn how to set up OpenClaw as a personalized AI agent #DataScience #AI #Python

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πŸ“Œ Advance Planning for AI Project Evaluation πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2026-02-17 | ⏱️ Read time: 7 min
πŸ“Œ Advance Planning for AI Project Evaluation πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2026-02-17 | ⏱️ Read time: 7 min read The work to do before the work begins #DataScience #AI #Python

πŸ“Œ Iron Triangles: Powerful Tools for Analyzing Trade-Offs in AI Product Development πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’
πŸ“Œ Iron Triangles: Powerful Tools for Analyzing Trade-Offs in AI Product Development πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2026-02-17 | ⏱️ Read time: 9 min read Conceptual overview and practical guidance #DataScience #AI #Python

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πŸ“Œ Building a LangGraph Agent from Scratch πŸ—‚ Category: AGENTIC AI πŸ•’ Date: 2026-02-17 | ⏱️ Read time: 10 min read Everything
πŸ“Œ Building a LangGraph Agent from Scratch πŸ—‚ Category: AGENTIC AI πŸ•’ Date: 2026-02-17 | ⏱️ Read time: 10 min read Everything you need to know to get started #DataScience #AI #Python

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πŸ“Œ The Strangest Bottleneck in Modern LLMs πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2026-02-16 | ⏱️ Read time: 11 min re
πŸ“Œ The Strangest Bottleneck in Modern LLMs πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2026-02-16 | ⏱️ Read time: 11 min read Why insanely fast GPUs still can’t make LLMs feel instant #DataScience #AI #Python