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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 40 334 subscribers, ranking 3 331 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 40 334 subscribers.

According to the latest data from 10 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 383 over the last 30 days and by 25 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.35%. 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 948 views. Within the first day, a publication typically gains 786 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 11 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.

40 334
Subscribers
+2524 hours
+1227 days
+38330 days
Posts Archive
πŸ“Œ Agentic AI 102: Guardrails and Agent Evaluation πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-05-16 | ⏱️ Read time: 1
πŸ“Œ Agentic AI 102: Guardrails and Agent Evaluation πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-05-16 | ⏱️ Read time: 12 min read An introduction to tools that make your model safer and more predictable and performant.

πŸ“Œ The Automation Trap: Why Low-Code AI Models Fail When You Scale πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-05-16 |
πŸ“Œ The Automation Trap: Why Low-Code AI Models Fail When You Scale πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-05-16 | ⏱️ Read time: 7 min read Low-code AI platforms promise speed, a model without a single line of code. But when…

πŸ“Œ How to Build an AI Journal with LlamaIndex πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-05-16 | ⏱️ Read time: 10 min
πŸ“Œ How to Build an AI Journal with LlamaIndex πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-05-16 | ⏱️ Read time: 10 min read A step-by-step guide for building an AI assistant powered by LlamaIndex

πŸ“Œ How to Set the Number of Trees in Random Forest πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-05-16 | ⏱️ Read time: 13 min r
πŸ“Œ How to Set the Number of Trees in Random Forest πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-05-16 | ⏱️ Read time: 13 min read A practical introduction to the optRF package

πŸ“Œ Optimizing Multi-Objective Problems with Desirability Functions πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-05-20 | ⏱️ Read ti
πŸ“Œ Optimizing Multi-Objective Problems with Desirability Functions πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-05-20 | ⏱️ Read time: 8 min read Applied to a very real problem: baking bread!

πŸ“Œ I Teach Data Viz with a Bag of Rocks πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-05-20 | ⏱️ Read time: 5 min read Here’s Why D
πŸ“Œ I Teach Data Viz with a Bag of Rocks πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-05-20 | ⏱️ Read time: 5 min read Here’s Why Domain-Specific Integration Matters in Your Data Science Workflows

πŸ“Œ What the Most Detailed Peer-Reviewed Study on AI in the Classroom Taught Us πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 20
πŸ“Œ What the Most Detailed Peer-Reviewed Study on AI in the Classroom Taught Us πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-05-20 | ⏱️ Read time: 8 min read A meta analysis that turns out positive yet identifies the need for further research

πŸ“Œ Building AI Applications in Ruby πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-05-21 | ⏱️ Read time: 15 min read Why
πŸ“Œ Building AI Applications in Ruby πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-05-21 | ⏱️ Read time: 15 min read Why Ruby may be the best language to write your next AI web application

πŸ“Œ Use PyTorch to Easily Access Your GPU πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-05-21 | ⏱️ Read time: 12 min read Or … how an
πŸ“Œ Use PyTorch to Easily Access Your GPU πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-05-21 | ⏱️ Read time: 12 min read Or β€¦ how an ML library can accelerate non-ML computations

πŸ“Œ Top Machine Learning Jobs and How to Prepare For Them πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-05-21 | ⏱️ Read time: 8
πŸ“Œ Top Machine Learning Jobs and How to Prepare For Them πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-05-21 | ⏱️ Read time: 8 min read Explaining the different machine learning roles

πŸ“Œ About Calculating Date Ranges in DAX πŸ—‚ Category: DATA ANALYSIS πŸ•’ Date: 2025-05-22 | ⏱️ Read time: 7 min read When perfor
πŸ“Œ About Calculating Date Ranges in DAX πŸ—‚ Category: DATA ANALYSIS πŸ•’ Date: 2025-05-22 | ⏱️ Read time: 7 min read When performing date calculations, creating date ranges can be helpful. But how can we do…

πŸ“Œ What Statistics Can Tell Us About NBA Coaches πŸ—‚ Category: πŸ•’ Date: 2025-05-22 | ⏱️ Read time: 10 min read Using Python to
πŸ“Œ What Statistics Can Tell Us About NBA Coaches πŸ—‚ Category: πŸ•’ Date: 2025-05-22 | ⏱️ Read time: 10 min read Using Python to determine where NBA coaches come from and what makes them successful

πŸ“Œ Inheritance: A Software Engineering Concept Data Scientists Must Know To Succeed πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-05
πŸ“Œ Inheritance: A Software Engineering Concept Data Scientists Must Know To Succeed πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-05-22 | ⏱️ Read time: 12 min read Coding concepts that distinguish an amateur from a professional data scientist

πŸ“Œ Google’s AlphaEvolve: Getting Started with Evolutionary Coding Agents πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-0
πŸ“Œ Google’s AlphaEvolve: Getting Started with Evolutionary Coding Agents πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-05-22 | ⏱️ Read time: 20 min read Introduction AlphaEvolve 1 is a promising new coding agent by Google’s DeepMind. Let’s look at…

πŸ“Œ Multiple Linear Regression Analysis πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-05-22 | ⏱️ Read time: 12 min read Implementati
πŸ“Œ Multiple Linear Regression Analysis πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-05-22 | ⏱️ Read time: 12 min read Implementation of multiple linear regression on real data: Assumption checks, model evaluation, and interpretation of…

πŸ“Œ How to Evaluate LLMs and Algorithms β€” The Right Way πŸ—‚ Category: THE VARIABLE πŸ•’ Date: 2025-05-23 | ⏱️ Read time: 3 min re
πŸ“Œ How to Evaluate LLMs and Algorithms β€” The Right Way πŸ—‚ Category: THE VARIABLE πŸ•’ Date: 2025-05-23 | ⏱️ Read time: 3 min read This week, we focus on the best strategies for evaluating and benchmarking the performance of…

πŸ“Œ Do More with NumPy Array Type Hints: Annotate & Validate Shape & Dtype πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-05-23 | ⏱️ R
πŸ“Œ Do More with NumPy Array Type Hints: Annotate & Validate Shape & Dtype πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-05-23 | ⏱️ Read time: 5 min read Improve static analysis and run-time validation with full generic specification

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πŸ“Œ Estimating Product-Level Price Elasticities Using Hierarchical Bayesian πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-05-23
πŸ“Œ Estimating Product-Level Price Elasticities Using Hierarchical Bayesian πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-05-23 | ⏱️ Read time: 21 min read Using one model to personalize ML results

πŸ“Œ Prototyping Gradient Descent in Machine Learning πŸ—‚ Category: πŸ•’ Date: 2025-05-23 | ⏱️ Read time: 10 min read Mathematical
πŸ“Œ Prototyping Gradient Descent in Machine Learning πŸ—‚ Category: πŸ•’ Date: 2025-05-23 | ⏱️ Read time: 10 min read Mathematical theorem and credit transaction prediction using Stochastic / Batch GD