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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 40 346 subscribers, ranking 3 329 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 40 346 subscribers.

According to the latest data from 11 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 393 over the last 30 days and by 17 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.29%. Within the first 24 hours after publication, content typically collects 1.74% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 924 views. Within the first day, a publication typically gains 702 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
  • 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 12 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.

40 346
Subscribers
+1724 hours
+1237 days
+39330 days
Posts Archive
πŸ“Œ Time Series Forecasting Made Simple (Part 3.1): STL Decomposition πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-09 | ⏱️ Read
πŸ“Œ Time Series Forecasting Made Simple (Part 3.1): STL Decomposition πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-09 | ⏱️ Read time: 24 min read STL Decomposition excels when seasonal patterns evolve over time.

πŸ“Œ How to Perform Effective Data Cleaning for Machine Learning πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-07-09 | ⏱️ Read ti
πŸ“Œ How to Perform Effective Data Cleaning for Machine Learning πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-07-09 | ⏱️ Read time: 11 min read Learn how you can improve your machine learning models using effective data cleaning

πŸ“Œ AI Agents Are Shaping the Future of Work Task by Task, Not Job by Job πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-0
πŸ“Œ AI Agents Are Shaping the Future of Work Task by Task, Not Job by Job πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-09 | ⏱️ Read time: 12 min read What two groundbreaking studies reveal about the future of human-AI collaboration, and the enterprise playbook…

πŸ“Œ Recap of all types of LLM Agents πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-09 | ⏱️ Read time: 6 min read Regular
πŸ“Œ Recap of all types of LLM Agents πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-09 | ⏱️ Read time: 6 min read Regular, ReAct, Chain-of-Thought, Reflexion, ToT, GoT, PoT

πŸ“Œ Work Data Is the Next Frontier for GenAI πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-09 | ⏱️ Read time: 17 min rea
πŸ“Œ Work Data Is the Next Frontier for GenAI πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-09 | ⏱️ Read time: 17 min read 9 reasons why work data is the single most valuable data source for LLM training,…

πŸ“Œ The Crucial Role of NUMA Awareness in High-Performance Deep Learning πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07
πŸ“Œ The Crucial Role of NUMA Awareness in High-Performance Deep Learning πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-10 | ⏱️ Read time: 16 min read PyTorch model performance analysis and optimization β€” Part 10

πŸ“Œ Worried About AI? Use It to Your Advantage πŸ—‚ Category: THE VARIABLE πŸ•’ Date: 2025-07-10 | ⏱️ Read time: 3 min read This w
πŸ“Œ Worried About AI? Use It to Your Advantage πŸ—‚ Category: THE VARIABLE πŸ•’ Date: 2025-07-10 | ⏱️ Read time: 3 min read This week, we focus on the future of data science and the opportunities that can…

πŸ“Œ Evaluation-Driven Development for LLM-Powered Products: Lessons from Building in Healthcare πŸ—‚ Category: LARGE LANGUAGE MO
πŸ“Œ Evaluation-Driven Development for LLM-Powered Products: Lessons from Building in Healthcare πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-10 | ⏱️ Read time: 30 min read How metrics and monitoring combine with human expertise to build trustworthy AI in healthcare.

πŸ“Œ Scene Understanding in Action: Real-World Validation of Multimodal AI Integration πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’
πŸ“Œ Scene Understanding in Action: Real-World Validation of Multimodal AI Integration πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-10 | ⏱️ Read time: 13 min read A deep dive into real-world case studies: from indoor space and urban streets to world-famous…

πŸ“Œ Reducing Time to Value for Data Science Projects: Part 3 πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-10 | ⏱️ Read time: 14
πŸ“Œ Reducing Time to Value for Data Science Projects: Part 3 πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-10 | ⏱️ Read time: 14 min read Setting up a robust experimentation process

πŸ“Œ Building a Π‘ustom MCP Chatbot πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-10 | ⏱️ Read time: 25 min read Underst
πŸ“Œ Building a Π‘ustom MCP Chatbot πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-10 | ⏱️ Read time: 25 min read Understanding all the details of the model context protocol

πŸ“Œ Hitchhiker’s Guide to RAG: From Tiny Files to Tolstoy with OpenAI’s API and LangChain πŸ—‚ Category: LARGE LANGUAGE MODELS οΏ½
πŸ“Œ Hitchhiker’s Guide to RAG: From Tiny Files to Tolstoy with OpenAI’s API and LangChain πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-11 | ⏱️ Read time: 9 min read Scaling a simple RAG pipeline from simple notes to full books

πŸ“Œ Are You Being Unfair to LLMs? πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-11 | ⏱️ Read time: 9 min read They may d
πŸ“Œ Are You Being Unfair to LLMs? πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-11 | ⏱️ Read time: 9 min read They may deserve better.

πŸ“Œ Let AI Tune Your Voice Assistant πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-14 | ⏱️ Read time: 29 min read A pr
πŸ“Œ Let AI Tune Your Voice Assistant πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-14 | ⏱️ Read time: 29 min read A practical guide to automating prompt engineering for voice assistants

πŸ“Œ The Age of Self-Evolving AI Is Here πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-17 | ⏱️ Read time: 17 min read How
πŸ“Œ The Age of Self-Evolving AI Is Here πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-17 | ⏱️ Read time: 17 min read How Meta’s latest breakthrough lets models learn, adapt, and improve β€” all on their own

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visua
This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages βœ… https://t.me/addlist/8_rRW2scgfRhOTc0 βœ… https://t.me/Codeprogrammer

πŸ“Œ Tracking Drill-Through Actions on Power BI Report Titles πŸ—‚ Category: POWER BI πŸ•’ Date: 2025-07-14 | ⏱️ Read time: 6 min r
πŸ“Œ Tracking Drill-Through Actions on Power BI Report Titles πŸ—‚ Category: POWER BI πŸ•’ Date: 2025-07-14 | ⏱️ Read time: 6 min read When you have a drill-through page that can be called from multiple pages, it could…

πŸ“Œ CLIP Model Overview : Unlocking the Power of Multimodal AI πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-07-14 | ⏱️ Read tim
πŸ“Œ CLIP Model Overviewβ€Š: β€ŠUnlocking the Power of Multimodal AI πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-07-14 | ⏱️ Read time: 7 min read The magic behind multimodal models unlocked through contrastive learning

πŸ“Œ Simple Guide to Multi-Armed Bandits: A Key Concept Before Reinforcement Learning πŸ—‚ Category: REINFORCEMENT LEARNING πŸ•’ Da
πŸ“Œ Simple Guide to Multi-Armed Bandits: A Key Concept Before Reinforcement Learning πŸ—‚ Category: REINFORCEMENT LEARNING πŸ•’ Date: 2025-07-14 | ⏱️ Read time: 12 min read How AI learns to make better decisions and why you should care about exploration vs.…

πŸ“Œ Dynamic Inventory Optimization with Censored Demand πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-14 | ⏱️ Read time: 20 min r
πŸ“Œ Dynamic Inventory Optimization with Censored Demand πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-14 | ⏱️ Read time: 20 min read A sequential decision framework with Bayesian learning