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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 40 208 subscribers, ranking 3 344 in the Technologies & Applications category and 228 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 40 208 subscribers.

According to the latest data from 03 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 338 over the last 30 days and by 9 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.04%. Within the first 24 hours after publication, content typically collects 2.42% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 822 views. Within the first day, a publication typically gains 973 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 04 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.

40 208
Subscribers
+924 hours
+727 days
+33830 days
Posts Archive
πŸ“Œ AI for the Absolute Novice – Intuitively and Exhaustively Explained πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-
πŸ“Œ AI for the Absolute Novice – Intuitively and Exhaustively Explained πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 40 min read From β€œI’ve never coded” to making an AI model from scratch.

πŸ“Œ KernelSHAP can be misleading with correlated predictors πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read
πŸ“Œ KernelSHAP can be misleading with correlated predictors πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 7 min read A concrete case study

πŸ“Œ Pre-Commit & Git Hooks: Automate High Code Quality πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read time
πŸ“Œ Pre-Commit & Git Hooks: Automate High Code Quality πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 6 min read How to improve your code quality with pre-commit and git hooks

πŸ“Œ Structured Outputs and How to Use Them πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 5 min read
πŸ“Œ Structured Outputs and How to Use Them πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 5 min read Building robustness and determinism in LLM applications

πŸ“Œ Improving Code Quality During Data Transformation with Polars πŸ—‚ Category: πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 6 min read
πŸ“Œ Improving Code Quality During Data Transformation with Polars πŸ—‚ Category: πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 6 min read Optimize your data workflows with Polars by improving code quality and refining transformations with these…

πŸ“Œ Running a SOTA 7B Parameter Embedding Model on a Single GPU πŸ—‚ Category: πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 19 min read I
πŸ“Œ Running a SOTA 7B Parameter Embedding Model on a Single GPU πŸ—‚ Category: πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 19 min read In this post I will explain how to run a state-of-the-art 7B parameter LLM based…

πŸ“Œ Algorithm-Agnostic Model Building with MLflow πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-10 | ⏱️ Read time: 10 min rea
πŸ“Œ Algorithm-Agnostic Model Building with MLflow πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-10 | ⏱️ Read time: 10 min read A beginner-friendly step-by-step guide to creating generic ML pipelines using mlflow.pyfunc

πŸ“Œ Data Scaling 101: Standardization and Min-Max Scaling Explained πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-08-10 | ⏱️ Rea
πŸ“Œ Data Scaling 101: Standardization and Min-Max Scaling Explained πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-08-10 | ⏱️ Read time: 5 min read When to use MinMaxScaler vs StandardScaler vs something else

πŸ“Œ Which Regression technique should you use? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-10 | ⏱️ Read time: 12 min
πŸ“Œ Which Regression technique should you use? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-10 | ⏱️ Read time: 12 min read Here’s a taxonomy of what is the best regression technique based on your specific dataset

πŸ“Œ Denormalisation: Thoughtful Optimisation or Irrational Avant-Garde? πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-10 | ⏱️ Rea
πŸ“Œ Denormalisation: Thoughtful Optimisation or Irrational Avant-Garde? πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-10 | ⏱️ Read time: 19 min read Perspective on Performance Optimisation and Data Quality

πŸ“Œ Introduction to Support Vector Machines - Motivation and Basics πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-10 | ⏱️ Read ti
πŸ“Œ Introduction to Support Vector Machinesβ€Š-β€ŠMotivation and Basics πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-10 | ⏱️ Read time: 8 min read Learn basic concepts that make Support Vector Machine a powerful linear classifier

πŸ“Œ Accelerating AI/ML Model Training with Custom Operators πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-11 | ⏱️ Read time:
πŸ“Œ Accelerating AI/ML Model Training with Custom Operators πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-11 | ⏱️ Read time: 18 min read On the potential benefits of creating model-specific GPU kernels and their application to optimizing the…

πŸ“Œ Top Career Websites for Data Engineers πŸ—‚ Category: ANALYTICS πŸ•’ Date: 2024-08-11 | ⏱️ Read time: 9 min read How to find f
πŸ“Œ Top Career Websites for Data Engineers πŸ—‚ Category: ANALYTICS πŸ•’ Date: 2024-08-11 | ⏱️ Read time: 9 min read How to find fantastic remote jobs and get hired

What if every notification meant free money? Kittu X Earning reveals secret hacks, daily loot, and real ways to grow your ear
What if every notification meant free money? Kittu X Earning reveals secret hacks, daily loot, and real ways to grow your earning game. Ready to spot the trick that others always miss? Don’t let easy cash slip by β€” hit join and become part of the earning empire today! Timing matters. Start earning now βž” Kittu X Earning πŸ’Έ #ad InsideAds

πŸ“Œ How to practice data analyst interviews with AI πŸ—‚ Category: πŸ•’ Date: 2024-08-12 | ⏱️ Read time: 8 min read Using LLMs to
πŸ“Œ How to practice data analyst interviews with AI πŸ—‚ Category: πŸ•’ Date: 2024-08-12 | ⏱️ Read time: 8 min read Using LLMs to generate synthetic data and code

πŸ“Œ AI Agents – From Concepts to Practical Implementation in Python πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-12 |
πŸ“Œ AI Agents – From Concepts to Practical Implementation in Python πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-12 | ⏱️ Read time: 12 min read This will change the way you think about AI and its capabilities

πŸ“Œ Advanced Recursive and Follow-Up Retrieval Techniques For Better RAGs πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-0
πŸ“Œ Advanced Recursive and Follow-Up Retrieval Techniques For Better RAGs πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-12 | ⏱️ Read time: 18 min read Breaking the problem solves half of it. Chaining them makes it even better.

πŸ“Œ The Poisson Bootstrap πŸ—‚ Category: STATISTICS πŸ•’ Date: 2024-08-12 | ⏱️ Read time: 10 min read Bootstrapping over large dat
πŸ“Œ The Poisson Bootstrap πŸ—‚ Category: STATISTICS πŸ•’ Date: 2024-08-12 | ⏱️ Read time: 10 min read Bootstrapping over large datasets

πŸ“Œ New Approach for Training Physical (as Opposed to Computer-Based) Artificial Neural Networks πŸ—‚ Category: ARTIFICIAL INTEL
πŸ“Œ New Approach for Training Physical (as Opposed to Computer-Based) Artificial Neural Networks πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-12 | ⏱️ Read time: 7 min read Neural networks built from light waves could allow for much more versatile, scalable, and energy-efficient…

πŸ“Œ LLM-Powered Parsing and Analysis of Semi-Structured & Structured Documents πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 202
πŸ“Œ LLM-Powered Parsing and Analysis of Semi-Structured & Structured Documents πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-08-12 | ⏱️ Read time: 20 min read This article shows how to extract desired or key information from semi-structured or unstructured information…