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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 40 244 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 40 244 subscribers.

According to the latest data from 05 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 346 over the last 30 days and by 22 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 1.97%. Within the first 24 hours after publication, content typically collects 1.86% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 794 views. Within the first day, a publication typically gains 749 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 06 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.

40 244
Subscribers
+2224 hours
+987 days
+34630 days
Posts Archive
πŸ“Œ Are You Sure You Want to Become a Data Science Manager? πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-22 | ⏱️ Read time: 18 m
πŸ“Œ Are You Sure You Want to Become a Data Science Manager? πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-22 | ⏱️ Read time: 18 min read Don’t rush into the fancy title until you have read this.

πŸ“Œ Another Hike Up Everest πŸ—‚ Category: πŸ•’ Date: 2024-11-22 | ⏱️ Read time: 9 min read How to make progress on hard problems
πŸ“Œ Another Hike Up Everest πŸ—‚ Category: πŸ•’ Date: 2024-11-22 | ⏱️ Read time: 9 min read How to make progress on hard problems in AI

πŸ“Œ LLM Routing – Intuitively and Exhaustively Explained πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-22 | ⏱️ Read ti
πŸ“Œ LLM Routing – Intuitively and Exhaustively Explained πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-22 | ⏱️ Read time: 69 min read Dynamically Choosing the Right LLM

πŸ“Œ Dynamic, Lazy Dependency Injection in Python πŸ—‚ Category: CODING πŸ•’ Date: 2024-11-22 | ⏱️ Read time: 7 min read Automatic
πŸ“Œ Dynamic, Lazy Dependency Injection in Python πŸ—‚ Category: CODING πŸ•’ Date: 2024-11-22 | ⏱️ Read time: 7 min read Automatic Python dependency injection to make your code more testable, decoupled, uncomplicated and readable

πŸ“Œ How Spotify Implemented Personalized Audiobook Recommendations πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-22 |
πŸ“Œ How Spotify Implemented Personalized Audiobook Recommendations πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-22 | ⏱️ Read time: 9 min read Personalized audiobook recommendations using graph neural networks

πŸ“Œ Don’t Be Afraid to Use Machine Learning for Simple Tasks πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-22 | ⏱️ Read time: 7 m
πŸ“Œ Don’t Be Afraid to Use Machine Learning for Simple Tasks πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-22 | ⏱️ Read time: 7 min read A common misconception across industries

πŸ“Œ Productionising GenAI Agents: Evaluating Tool Selection with Automated Testing πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024
πŸ“Œ Productionising GenAI Agents: Evaluating Tool Selection with Automated Testing πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-22 | ⏱️ Read time: 21 min read How to create reliable and scalable GenAI Agents for real-world applications

πŸ“Œ Documenting Python Projects with MkDocs πŸ—‚ Category: πŸ•’ Date: 2024-11-22 | ⏱️ Read time: 9 min read Use Markdown to quickl
πŸ“Œ Documenting Python Projects with MkDocs πŸ—‚ Category: πŸ•’ Date: 2024-11-22 | ⏱️ Read time: 9 min read Use Markdown to quickly create a beautiful documentation page for your projects

πŸ“Œ Engineering the Future: Common Threads in Data, Software, and Artificial Intelligence πŸ—‚ Category: ARTIFICIAL INTELLIGENCE
πŸ“Œ Engineering the Future: Common Threads in Data, Software, and Artificial Intelligence πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-23 | ⏱️ Read time: 8 min read How recognizing cross-discipline commonalities not only enhances recruitment strategies but also supports adaptable IT architectures.

πŸ“Œ Implementing Streamlit-Authenticator Across Multi-Page Apps πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-23 | ⏱️ Read time:
πŸ“Œ Implementing Streamlit-Authenticator Across Multi-Page Apps πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-23 | ⏱️ Read time: 7 min read Streamlit-Authenticator allows you to add a simple yet robust method for user authentication in a…

πŸ“Œ Confidence Interval vs. Prediction Interval πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-24 | ⏱️ Read time: 9 min read A sma
πŸ“Œ Confidence Interval vs. Prediction Interval πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-24 | ⏱️ Read time: 9 min read A small but important difference that you should know

πŸ“Œ The Difference Between ML Engineers and Data Scientists πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-24 | ⏱️ Read
πŸ“Œ The Difference Between ML Engineers and Data Scientists πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-24 | ⏱️ Read time: 6 min read Helping you decide whether you want to be a data scientist or machine learning engineer

πŸ“Œ Perform Outlier Detection More Effectively Using Subsets of Features πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-24 | ⏱️ Re
πŸ“Œ Perform Outlier Detection More Effectively Using Subsets of Features πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-24 | ⏱️ Read time: 38 min read Identify relevant subspaces: subsets of features that allow you to most effectively perform outlier detection…

πŸ“Œ Step-by-Step Guide for Building Waffle Charts in Plotly πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-25 | ⏱️ Read time: 13 m
πŸ“Œ Step-by-Step Guide for Building Waffle Charts in Plotly πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-25 | ⏱️ Read time: 13 min read Learn how to create custom waffle charts in Python using Plotly for data visualization

πŸ“Œ Bias-Variance Tradeoff, Explained: A Visual Guide with Code Examples for Beginners πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date:
πŸ“Œ Bias-Variance Tradeoff, Explained: A Visual Guide with Code Examples for Beginners πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-25 | ⏱️ Read time: 22 min read How underfitting and overfitting fight over your models

πŸ“Œ Why Batch Normalization Matters for Deep Learning πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-11-25 | ⏱️ Read time: 13 min re
πŸ“Œ Why Batch Normalization Matters for Deep Learning πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-11-25 | ⏱️ Read time: 13 min read Discover the role of batch normalization in streamlining neural network training and improving model performance

πŸ“Œ Trapped in the Net: Where is a Foundation Model for Graphs? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-25 | ⏱️
πŸ“Œ Trapped in the Net: Where is a Foundation Model for Graphs? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-25 | ⏱️ Read time: 12 min read Disconnected from the other modalities graphs wait for their AI revolution: is it coming?

πŸ“Œ Dog Poop Compass: Bayesian Analysis of Canine Business πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-25 | ⏱️ Read time: 23 mi
πŸ“Œ Dog Poop Compass: Bayesian Analysis of Canine Business πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-25 | ⏱️ Read time: 23 min read A Bayesian analysis of canine business

πŸ“Œ Building a Knowledge Graph From Scratch Using LLMs πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-25 | ⏱️ Read time
πŸ“Œ Building a Knowledge Graph From Scratch Using LLMs πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-25 | ⏱️ Read time: 46 min read Turn your Pandas Data Frame into a Knowledge Graph using LLMs. Build your own LLM…

πŸ“Œ Deploying a PICO Extractor in Five Steps πŸ—‚ Category: NATURAL LANGUAGE PROCESSING πŸ•’ Date: 2025-09-19 | ⏱️ Read time: 8 mi
πŸ“Œ Deploying a PICO Extractor in Five Steps πŸ—‚ Category: NATURAL LANGUAGE PROCESSING πŸ•’ Date: 2025-09-19 | ⏱️ Read time: 8 min read Lessons learned deploying a domain-specific NER model