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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 40 323 subscribers, ranking 3 332 in the Technologies & Applications category and 225 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 40 323 subscribers.

According to the latest data from 09 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 378 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 2.23%. Within the first 24 hours after publication, content typically collects 1.95% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 897 views. Within the first day, a publication typically gains 788 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 10 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.

40 323
Subscribers
+3024 hours
+1067 days
+37830 days
Posts Archive
πŸ“Œ A Clear Intro to MCP (Model Context Protocol) with Code Examples πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-03-25
πŸ“Œ A Clear Intro to MCP (Model Context Protocol) with Code Examples πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-03-25 | ⏱️ Read time: 16 min read MCP is a way to democratize access to tools for AI Agents. In this article…

πŸ“Œ Testing the Power of Multimodal AI Systems in Reading and Interpreting Photographs, Maps, Charts and More πŸ—‚ Category: LAR
πŸ“Œ Testing the Power of Multimodal AI Systems in Reading and Interpreting Photographs, Maps, Charts and More πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-03-25 | ⏱️ Read time: 30 min read Can multimodal AI systems consisting in LLMs with vision capabilities understand figures and extract information…

πŸ“Œ Data-Driven March Madness Predictions πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-03-25 | ⏱️ Read time: 11 min read How to opt
πŸ“Œ Data-Driven March Madness Predictions πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-03-25 | ⏱️ Read time: 11 min read How to optimize your bracket systematically, no college basketball knowledge required

πŸ“Œ Attractors in Neural Network Circuits: Beauty and Chaos πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-03-25 | ⏱️ Read time:
πŸ“Œ Attractors in Neural Network Circuits: Beauty and Chaos πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-03-25 | ⏱️ Read time: 12 min read Neural networks under a different lens: generating basins of attraction in a shift register NN

πŸ“Œ The Ultimate AI/ML Roadmap For Beginners πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-03-25 | ⏱️ Read time: 10 min r
πŸ“Œ The Ultimate AI/ML Roadmap For Beginners πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-03-25 | ⏱️ Read time: 10 min read How to learn AI/ML from scratch

πŸ“Œ Uncertainty Quantification in Machine Learning with an Easy Python Interface πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-0
πŸ“Œ Uncertainty Quantification in Machine Learning with an Easy Python Interface πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-03-26 | ⏱️ Read time: 15 min read The ML Uncertainty Package

πŸ“Œ AI Agents from Scratch: Iterations & Chains πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-03-26 | ⏱️ Read time: 7 min
πŸ“Œ AI Agents from Scratch: Iterations & Chains πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-03-26 | ⏱️ Read time: 7 min read From Zero to Hero using only Python & Ollama (no GPU, no APIKEY)

πŸ“Œ Automate Supply Chain Analytics Workflows with AI Agents using n8n πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-03-2
πŸ“Œ Automate Supply Chain Analytics Workflows with AI Agents using n8n πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-03-26 | ⏱️ Read time: 6 min read What if you could automate complete supply chain analytics workflows β€Šwith low-code solutions?

πŸ“Œ About Towards Data Science πŸ—‚ Category: ABOUT πŸ•’ Date: 2025-03-27 | ⏱️ Read time: 2 min read We strive to present well-wri
πŸ“Œ About Towards Data Science πŸ—‚ Category: ABOUT πŸ•’ Date: 2025-03-27 | ⏱️ Read time: 2 min read We strive to present well-written, informative articles that our audience is excited to read.

πŸ“Œ How to Streamline Your Work with Agents and LLMs πŸ—‚ Category: THE VARIABLE πŸ•’ Date: 2025-03-27 | ⏱️ Read time: 3 min read
πŸ“Œ How to Streamline Your Work with Agents and LLMs πŸ—‚ Category: THE VARIABLE πŸ•’ Date: 2025-03-27 | ⏱️ Read time: 3 min read This week, we focus on helping you improve your workflow with AI.

πŸ“Œ Talk to Videos πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-03-27 | ⏱️ Read time: 28 min read Developing an interactiv
πŸ“Œ Talk to Videos πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-03-27 | ⏱️ Read time: 28 min read Developing an interactive AI application for video-based learning in education and business

πŸ“Œ Japanese-Chinese Translation with GenAI: What Works and What Doesn’t πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-03
πŸ“Œ Japanese-Chinese Translation with GenAI: What Works and What Doesn’t πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-03-27 | ⏱️ Read time: 20 min read Evaluating GenAI in Japanese-Chinese translation: current limits and opportunities

πŸ“Œ Data Science: From School to Work, Part III πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-03-27 | ⏱️ Read time: 12 min read Good
πŸ“Œ Data Science: From School to Work, Part III πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-03-27 | ⏱️ Read time: 12 min read Good practices for Python error handling and logging

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πŸ“Œ From Physics to Probability: Hamiltonian Mechanics for Generative Modeling and MCMC πŸ—‚ Category: MATH πŸ•’ Date: 2025-03-28
πŸ“Œ From Physics to Probability: Hamiltonian Mechanics for Generative Modeling and MCMC πŸ—‚ Category: MATH πŸ•’ Date: 2025-03-28 | ⏱️ Read time: 17 min read Hamiltonian mechanics is a way to describe how physical systems, like planets or pendulums, move…

πŸ“Œ AI Agents from Scratch: Multi-Agent System πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-03-28 | ⏱️ Read time: 14 min
πŸ“Œ AI Agents from Scratch: Multi-Agent System πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-03-28 | ⏱️ Read time: 14 min read From Zero to Hero using only Python & Ollama (no GPU, no APIKEY)

πŸ“Œ Master the 3D Reconstruction Process: A Step-by-Step Guide πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-03-28 | ⏱️ Read time: 1
πŸ“Œ Master the 3D Reconstruction Process: A Step-by-Step Guide πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-03-28 | ⏱️ Read time: 17 min read Learn the complete 3D reconstruction pipeline from feature extraction to dense matching. Master photogrammetry with…

πŸ“Œ A Little More Conversation, A Little Less Action β€” A Case Against Premature Data Integration πŸ—‚ Category: DATA SCIENCE πŸ•’
πŸ“Œ A Little More Conversation, A Little Less Action β€” A Case Against Premature Data Integration πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-03-28 | ⏱️ Read time: 14 min read Running a large data integration project before embarking on the ML part is easily a…

πŸ“Œ The Art of Hybrid Architectures πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-03-28 | ⏱️ Read time: 32 min read Combi
πŸ“Œ The Art of Hybrid Architectures πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-03-28 | ⏱️ Read time: 32 min read Combining CNNs and Transformers to Elevate Fine-Grained Visual Classification

πŸ“Œ Understanding the Tech Stack Behind Generative AI πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-03-31 | ⏱️ Read time:
πŸ“Œ Understanding the Tech Stack Behind Generative AI πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-03-31 | ⏱️ Read time: 22 min read From foundation models to vector databases and AI agents β€” what makes modern AI work