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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 40 373 subscribers, ranking 3 327 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 40 373 subscribers.

According to the latest data from 12 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 399 over the last 30 days and by 24 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.42%. 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 979 views. Within the first day, a publication typically gains 703 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 13 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.

40 373
Subscribers
+2424 hours
+1257 days
+39930 days
Posts Archive
πŸ“Œ Why Science Must Embrace Co-Creation with Generative AI to Break Current Research Barriers πŸ—‚ Category: ARTIFICIAL INTELLI
πŸ“Œ Why Science Must Embrace Co-Creation with Generative AI to Break Current Research Barriers πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-08-25 | ⏱️ Read time: 24 min read An Open Letter to the Scientific Community

πŸ“Œ How to Benchmark Classical Machine Learning Workloads on Google Cloud πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-08-25 |
πŸ“Œ How to Benchmark Classical Machine Learning Workloads on Google Cloud πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-08-25 | ⏱️ Read time: 8 min read Harnessing CPUs for Practical, Cost-Effective Machine Learning

πŸ“Œ Why Your Prompts Don’t Belong in Git πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-08-25 | ⏱️ Read time: 5 min read The
πŸ“Œ Why Your Prompts Don’t Belong in Git πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-08-25 | ⏱️ Read time: 5 min read The hidden cost of storing prompts in your source code

πŸ“Œ LLM Monitoring and Observability: Hands-on with Langfuse πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-08-25 | ⏱️ Read
πŸ“Œ LLM Monitoring and Observability: Hands-on with Langfuse πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-08-25 | ⏱️ Read time: 22 min read Learn the fundamentals of LLM monitoring and observability, from tracing to evaluation and setting up…

πŸ“Œ Google’s URL Context Grounding: Another Nail in RAG’s Coffin? πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-08-26 | ⏱️
πŸ“Œ Google’s URL Context Grounding: Another Nail in RAG’s Coffin? πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-08-26 | ⏱️ Read time: 13 min read Google’s hot streak in AI-related releases continues unabated. Just a few days ago, it released…

πŸ“Œ Using Google’s LangExtract and Gemma for Structured Data Extraction πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-08-26 | ⏱️ Rea
πŸ“Œ Using Google’s LangExtract and Gemma for Structured Data Extraction πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-08-26 | ⏱️ Read time: 9 min read Extracting structured information effectively and accurately from long unstructured text with LangExtract and LLMs

πŸ“Œ Plato’s Cave and the Shadows of Data πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-08-26 | ⏱️ Read time: 4 min read On truth, il
πŸ“Œ Plato’s Cave and the Shadows of Data πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-08-26 | ⏱️ Read time: 4 min read On truth, illusion, and the limits of what data can reveal

πŸ“Œ How to Develop Powerful Internal LLM Benchmarks πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-08-26 | ⏱️ Read time: 7 m
πŸ“Œ How to Develop Powerful Internal LLM Benchmarks πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-08-26 | ⏱️ Read time: 7 min read Learn how to compare LLMs using your own interal benchmark

πŸ“Œ The Math You Need to Pan and Tilt 360Β° Images πŸ—‚ Category: MATH πŸ•’ Date: 2025-08-27 | ⏱️ Read time: 12 min read Panning a
πŸ“Œ The Math You Need to Pan and Tilt 360Β° Images πŸ—‚ Category: MATH πŸ•’ Date: 2025-08-27 | ⏱️ Read time: 12 min read Panning a spherical image is just a horizontal roll, but tilting it vertically is much…

πŸ“Œ A Brief History of GPT Through Papers πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-08-27 | ⏱️ Read time: 16 min read L
πŸ“Œ A Brief History of GPT Through Papers πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-08-27 | ⏱️ Read time: 16 min read Language models are becoming really good. But where did they come from?

πŸ“Œ Time Series Forecasting Made Simple (Part 4.1): Understanding Stationarity in a Time Series πŸ—‚ Category: DATA SCIENCE πŸ•’ D
πŸ“Œ Time Series Forecasting Made Simple (Part 4.1): Understanding Stationarity in a Time Series πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-08-27 | ⏱️ Read time: 11 min read An intuitive guide to stationarity in a time series

πŸ“Œ Everything I Studied to Become a Machine Learning Engineer (No CS Background) πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-
πŸ“Œ Everything I Studied to Become a Machine Learning Engineer (No CS Background) πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-08-27 | ⏱️ Read time: 9 min read The books, courses, and resources I used in my journey.

πŸ“Œ Get AI-Ready: How to Prepare for a World of Agentic AI as Tech Professionals πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date:
πŸ“Œ Get AI-Ready: How to Prepare for a World of Agentic AI as Tech Professionals πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-08-27 | ⏱️ Read time: 6 min read Explore how Agentic AI is reshaping the tech careers, from data to decision-making, and how…

πŸ“Œ Air for Tomorrow: Why Openness in Air Quality Research and Implementation Matters for Global Equity πŸ—‚ Category: DATA SCIE
πŸ“Œ Air for Tomorrow: Why Openness in Air Quality Research and Implementation Matters for Global Equity πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-08-27 | ⏱️ Read time: 10 min read Understand how open source can help you unravel air quality

πŸ“Œ August Must-Reads: LLM Costs, Research Agents, and More πŸ—‚ Category: THE VARIABLE πŸ•’ Date: 2025-08-28 | ⏱️ Read time: 2 mi
πŸ“Œ August Must-Reads: LLM Costs, Research Agents, and More πŸ—‚ Category: THE VARIABLE πŸ•’ Date: 2025-08-28 | ⏱️ Read time: 2 min read Our most-read and -shared stories of the past month

πŸ“Œ A Visual Guide to Tuning Decision-Tree Hyperparameters πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-08-28 | ⏱️ Read time: 11 mi
πŸ“Œ A Visual Guide to Tuning Decision-Tree Hyperparameters πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-08-28 | ⏱️ Read time: 11 min read How hyperparameter tuning visually changes decision trees

πŸ“Œ Graph Coloring for Data Science: A Comprehensive Guide πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-08-28 | ⏱️ Read time: 11 mi
πŸ“Œ Graph Coloring for Data Science: A Comprehensive Guide πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-08-28 | ⏱️ Read time: 11 min read From theoretical puzzles to practical applications

πŸ“Œ Stepwise Selection Made Simple: Improve Your Regression Models in Python πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-08-28
πŸ“Œ Stepwise Selection Made Simple: Improve Your Regression Models in Python πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-08-28 | ⏱️ Read time: 21 min read Dimensionality reduction in linear regression: classical stepwise methods and a Python application on real-world data

πŸ“Œ Implementing the Hangman Game in Python πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-08-28 | ⏱️ Read time: 11 min read A beginne
πŸ“Œ Implementing the Hangman Game in Python πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-08-28 | ⏱️ Read time: 11 min read A beginner-friendly project to understand variables, loops, and conditions in Python

πŸ“Œ How to Import Pre-Annotated Data into Label Studio and Run the Full Stack with Docker πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2
πŸ“Œ How to Import Pre-Annotated Data into Label Studio and Run the Full Stack with Docker πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-08-29 | ⏱️ Read time: 9 min read From VOC to JSON: Importing pre-annotations made simple