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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 How I Built and Deployed an App in 2 days with Lovable, Supabase, and Netlify πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-09-18
πŸ“Œ How I Built and Deployed an App in 2 days with Lovable, Supabase, and Netlify πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-09-18 | ⏱️ Read time: 8 min read All ideas can be turned into action in a matter of time now.

πŸ“Œ Overcoming Security Challenges in Protecting Shared Generative AI Environments πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Dat
πŸ“Œ Overcoming Security Challenges in Protecting Shared Generative AI Environments πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-02 | ⏱️ Read time: 15 min read Ensuring Safe AI on Multi-Tenancy

πŸ“Œ Machine Learning Experiments Done Right πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-02 | ⏱️ Read time: 8 min read A detaile
πŸ“Œ Machine Learning Experiments Done Right πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-02 | ⏱️ Read time: 8 min read A detailed guideline for designing machine learning experiments that produce reliable, reproducible results.

πŸ“Œ How to Build Prompt Engineering Expertise at Your Company πŸ—‚ Category: LEADERSHIP AND MANAGEMENT πŸ•’ Date: 2024-12-02 | ⏱️
πŸ“Œ How to Build Prompt Engineering Expertise at Your Company πŸ—‚ Category: LEADERSHIP AND MANAGEMENT πŸ•’ Date: 2024-12-02 | ⏱️ Read time: 9 min read You decided to employ generative AI at your company and have already conducted initial experiments…

πŸ“Œ Context-Aided Forecasting: Enhancing Forecasting with Textual Data πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-0
πŸ“Œ Context-Aided Forecasting: Enhancing Forecasting with Textual Data πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-02 | ⏱️ Read time: 9 min read A promising alternative approach to improve forecasting

πŸ“Œ Paper Walkthrough: Neural Style Transfer πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-12-03 | ⏱️ Read time: 28 min read Turn y
πŸ“Œ Paper Walkthrough: Neural Style Transfer πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-12-03 | ⏱️ Read time: 28 min read Turn your photos into paintings with deep learning – Implementing NST from scratch using PyTorch

πŸ“Œ The Ten Commandments for (Power BI) Reporting πŸ—‚ Category: πŸ•’ Date: 2024-12-03 | ⏱️ Read time: 12 min read I have been ask
πŸ“Œ The Ten Commandments for (Power BI) Reporting πŸ—‚ Category: πŸ•’ Date: 2024-12-03 | ⏱️ Read time: 12 min read I have been asked about some basic rules when creating a reporting solution. Here I…

πŸ“Œ The Name That Broke ChatGPT: Who is David Mayer? πŸ—‚ Category: πŸ•’ Date: 2024-12-03 | ⏱️ Read time: 20 min read When a buddy
πŸ“Œ The Name That Broke ChatGPT: Who is David Mayer? πŸ—‚ Category: πŸ•’ Date: 2024-12-03 | ⏱️ Read time: 20 min read When a buddy suggested I try putting the name β€œDavid Mayer” into ChatGPT, I didn’t think much…

πŸ“Œ Should you switch from VSCode to Cursor? πŸ—‚ Category: πŸ•’ Date: 2024-12-03 | ⏱️ Read time: 8 min read My experience using V
πŸ“Œ Should you switch from VSCode to Cursor? πŸ—‚ Category: πŸ•’ Date: 2024-12-03 | ⏱️ Read time: 8 min read My experience using VSCode (GitHub Copilot) and Cursor (Claude 3.5 Sonnet) as a Data Scientist.

πŸ“Œ The Cultural Impact of AI Generated Content: Part 1 πŸ—‚ Category: GENERATIVE AI πŸ•’ Date: 2024-12-03 | ⏱️ Read time: 9 min r
πŸ“Œ The Cultural Impact of AI Generated Content: Part 1 πŸ—‚ Category: GENERATIVE AI πŸ•’ Date: 2024-12-03 | ⏱️ Read time: 9 min read What happens when AI generated media becomes ubiquitous in our lives? How does this relate…

πŸ“Œ From Retrieval to Intelligence: Exploring RAG, Agent+RAG, and Evaluation with TruLens πŸ—‚ Category: πŸ•’ Date: 2024-12-03 | ⏱
πŸ“Œ From Retrieval to Intelligence: Exploring RAG, Agent+RAG, and Evaluation with TruLens πŸ—‚ Category: πŸ•’ Date: 2024-12-03 | ⏱️ Read time: 26 min read Unlocking the Power of GPT-Generated Private Corpora

πŸ“Œ Becoming a Data Scientist: What I Would Do If I Had to Start Over πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-03 | ⏱️ Read
πŸ“Œ Becoming a Data Scientist: What I Would Do If I Had to Start Over πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-03 | ⏱️ Read time: 12 min read Breaking into data science: The Good, the Bad, and the Python Bugs

πŸ“Œ Query Optimization for Mere Humans in PostgreSQL πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-12-03 | ⏱️ Read time: 9 min r
πŸ“Œ Query Optimization for Mere Humans in PostgreSQL πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-12-03 | ⏱️ Read time: 9 min read Understanding a PostgreSQL execution plan with practical examples

πŸ“Œ LLMs for Coding in 2024: Price, Performance, and the Battle for the Best πŸ—‚ Category: CODING πŸ•’ Date: 2024-12-04 | ⏱️ Read
πŸ“Œ LLMs for Coding in 2024: Price, Performance, and the Battle for the Best πŸ—‚ Category: CODING πŸ•’ Date: 2024-12-04 | ⏱️ Read time: 13 min read Evaluating the current LLM landscape based both benchmarks and real-world insights to help you make…

πŸ“Œ Training Language Models on Google Colab πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-12-04 | ⏱️ Read time: 5 min read A guide
πŸ“Œ Training Language Models on Google Colab πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-12-04 | ⏱️ Read time: 5 min read A guide to iterative fine-tuning and serialisation

πŸ“Œ Information at a Glance: Do Your Charts Suck? πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2024-12-04 | ⏱️ Read time: 9 min re
πŸ“Œ Information at a Glance: Do Your Charts Suck? πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2024-12-04 | ⏱️ Read time: 9 min read How pre-attentive processing, Gestalt theory, and visual data encoding inform data design decisions

πŸ“Œ Machine Learning Basics I Look for in Data Scientist Interviews πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-04 |
πŸ“Œ Machine Learning Basics I Look for in Data Scientist Interviews πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-04 | ⏱️ Read time: 50 min read Let’s build our breadth of science together.

πŸ“Œ An Introduction to CTEs in SQL πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2024-12-04 | ⏱️ Read time: 6 min read Explore how Common
πŸ“Œ An Introduction to CTEs in SQL πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2024-12-04 | ⏱️ Read time: 6 min read Explore how Common Table Expression (CTE) can help optimize SQL performance and readability

πŸ“Œ Step-by-Step Guide for Building Bump Charts in Plotly πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-04 | ⏱️ Read time: 13 min
πŸ“Œ Step-by-Step Guide for Building Bump Charts in Plotly πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-04 | ⏱️ Read time: 13 min read Learn how to create custom bump charts in Python using Plotly for data visualization