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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 40 255 subscribers, ranking 3 343 in the Technologies & Applications category and 227 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 40 255 subscribers.

According to the latest data from 06 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 336 over the last 30 days and by -4 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.25%. Within the first 24 hours after publication, content typically collects 1.88% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 906 views. Within the first day, a publication typically gains 758 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 07 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.

40 255
Subscribers
-424 hours
+917 days
+33630 days
Posts Archive
πŸ“Œ Why Retrieval-Augmented Generation Is Still Relevant in the Era of Long-Context Language Models πŸ—‚ Category: ARTIFICIAL IN
πŸ“Œ Why Retrieval-Augmented Generation Is Still Relevant in the Era of Long-Context Language Models πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-12 | ⏱️ Read time: 7 min read In this article we will explore why 128K tokens and more models can’t fully replace…

πŸ“Œ Bayes’ Theorem: Understanding Business Outcomes with Evidence πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-12 | ⏱️ Read time
πŸ“Œ Bayes’ Theorem: Understanding Business Outcomes with Evidence πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-12 | ⏱️ Read time: 11 min read A practical introduction to Bayes’ Theorem: Probability for Data Science Series (2)

πŸ“Œ I’m Doing the Advent of Code 2024 in Python – Day 3 πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-13 | ⏱️ Read time: 5 min re
πŸ“Œ I’m Doing the Advent of Code 2024 in Python – Day 3 πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-13 | ⏱️ Read time: 5 min read Let’s see how many stars we’ll collect.

πŸ“Œ Addressing the Butterfly Effect: Data Assimilation Using Ensemble Kalman Filter πŸ—‚ Category: πŸ•’ Date: 2024-12-13 | ⏱️ Read
πŸ“Œ Addressing the Butterfly Effect: Data Assimilation Using Ensemble Kalman Filter πŸ—‚ Category: πŸ•’ Date: 2024-12-13 | ⏱️ Read time: 9 min read Learn how to implement the Ensemble Kalman Filter for data assimilation, with mathematical details step-by-step…

πŸ“Œ Efficient Large Dimensional Self-Organising Maps with PyTorch πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-13 | ⏱
πŸ“Œ Efficient Large Dimensional Self-Organising Maps with PyTorch πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-13 | ⏱️ Read time: 6 min read Because it’s fun to self-organise

πŸ“Œ How I’d Learn AI in 2025 (If I Could Start Over) πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-12-13 | ⏱️ Read time: 7 min r
πŸ“Œ How I’d Learn AI in 2025 (If I Could Start Over) πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-12-13 | ⏱️ Read time: 7 min read A 5-step roadmap for today’s landscape

πŸ“Œ Agentic AI: Building Autonomous Systems from Scratch πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-13 | ⏱️ Read ti
πŸ“Œ Agentic AI: Building Autonomous Systems from Scratch πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-13 | ⏱️ Read time: 16 min read A Step-by-Step Guide to Creating Multi-Agent Frameworks in the Age of Generative AI

πŸ“Œ Ranking Basics: Pointwise, Pairwise, Listwise πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-14 | ⏱️ Read time: 8 min read Bec
πŸ“Œ Ranking Basics: Pointwise, Pairwise, Listwise πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-14 | ⏱️ Read time: 8 min read Because thy neighbour matters

πŸ“Œ Let’s Learn a Little About Computer Vision via Sudoku πŸ—‚ Category: πŸ•’ Date: 2024-12-14 | ⏱️ Read time: 9 min read Solving
πŸ“Œ Let’s Learn a Little About Computer Vision via Sudoku πŸ—‚ Category: πŸ•’ Date: 2024-12-14 | ⏱️ Read time: 9 min read Solving Sudoku is a fun challenge for coding, and adding computer vision to populate the…

πŸ“Œ A Design Researcher’s Guide to Publishing πŸ—‚ Category: DESIGN πŸ•’ Date: 2024-12-15 | ⏱️ Read time: 10 min read Turn β€˜publis
πŸ“Œ A Design Researcher’s Guide to Publishing πŸ—‚ Category: DESIGN πŸ•’ Date: 2024-12-15 | ⏱️ Read time: 10 min read Turn β€˜publish or perish’ into β€˜learn, write, and share’

πŸ“Œ Credit Card Fraud Detection with Different Sampling Techniques πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-15 | ⏱️ Read tim
πŸ“Œ Credit Card Fraud Detection with Different Sampling Techniques πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-15 | ⏱️ Read time: 10 min read How to deal with imbalanced data

πŸ“Œ The Essential Guide to R and Python Libraries for Data Visualization πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-16 | ⏱️ Re
πŸ“Œ The Essential Guide to R and Python Libraries for Data Visualization πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-16 | ⏱️ Read time: 19 min read Let’s dive into the most important libraries in R and Python to visualise data and…

πŸ“Œ Structured LLM Output Using Ollama πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 11 min read Co
πŸ“Œ Structured LLM Output Using Ollama πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 11 min read Control your model responses effectively

πŸ“Œ USGS DEM Files: How to Load, Merge, and Crop with Python πŸ—‚ Category: πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 12 min read A qu
πŸ“Œ USGS DEM Files: How to Load, Merge, and Crop with Python πŸ—‚ Category: πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 12 min read A quick guide to prepping digital elevation data

πŸ“Œ The Good, the Bad, and the Ugly: Memory for a Neural Network πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-17 | ⏱️
πŸ“Œ The Good, the Bad, and the Ugly: Memory for a Neural Network πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 13 min read Memory can play tricks, to learn best it is not always good to memorize

πŸ“Œ My Experiments with NotebookLM for Teaching πŸ—‚ Category: LLM APPLICATIONS πŸ•’ Date: 2025-09-16 | ⏱️ Read time: 8 min read E
πŸ“Œ My Experiments with NotebookLM for Teaching πŸ—‚ Category: LLM APPLICATIONS πŸ•’ Date: 2025-09-16 | ⏱️ Read time: 8 min read Exploring NotebookLM as a teaching companion

πŸ“Œ Using Python to Build a Calculator πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-09-16 | ⏱️ Read time: 5 min read A beginner-frie
πŸ“Œ Using Python to Build a Calculator πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-09-16 | ⏱️ Read time: 5 min read A beginner-friendly Python project to understand conditional statements, loops and recursive functions

πŸ“Œ Building a Unified Intent Recognition Engine πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-09-16 | ⏱️ Read time: 6 mi
πŸ“Œ Building a Unified Intent Recognition Engine πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-09-16 | ⏱️ Read time: 6 min read How modular design can simplify and scale intent classification in enterprise AI systems

πŸ“Œ A Case for Bagging and Boosting as Data Scientists’ Best Friends πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-17
πŸ“Œ A Case for Bagging and Boosting as Data Scientists’ Best Friends πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 16 min read Leveraging wisdom of the crowd in ML models.

πŸ“Œ Four Signs It’s Time to Leave Your Data Science Job πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 7 min r
πŸ“Œ Four Signs It’s Time to Leave Your Data Science Job πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 7 min read Four tell-tale signs that you should look for another job