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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 40 365 subscribers, ranking 3 329 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 40 365 subscribers.

According to the latest data from 11 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 393 over the last 30 days and by 17 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.29%. Within the first 24 hours after publication, content typically collects 1.74% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 924 views. Within the first day, a publication typically gains 702 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 12 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.

40 365
Subscribers
+1724 hours
+1237 days
+39330 days
Posts Archive
πŸ“Œ The End-to-End Data Scientist’s Prompt Playbook πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-09-08 | ⏱️ Read time: 10
πŸ“Œ The End-to-End Data Scientist’s Prompt Playbook πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-09-08 | ⏱️ Read time: 10 min read Part 3: Prompts for docs, DevOps, and stakeholder communication

β€œIt’s time to get these done.” Why are US lawmakers suddenly rushing crypto bills? What’s NEXT after BlackRock bought $416M i
β€œIt’s time to get these done.” Why are US lawmakers suddenly rushing crypto bills? What’s NEXT after BlackRock bought $416M in Bitcoin? No one tells you the full story – except here. The next move is days away. πŸ‘‰ Find out first #Ψ₯ΨΉΩ„Ψ§Ω† InsideAds

πŸ“Œ Become a Better Data Scientist with These Prompt Engineering Tips and Tricks πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2
πŸ“Œ Become a Better Data Scientist with These Prompt Engineering Tips and Tricks πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-06-30 | ⏱️ Read time: 11 min read Part 1: prompt engineering for planning, cleaning, and EDA

πŸ“Œ From Pixels to Plots πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-06-30 | ⏱️ Read time: 16 min read How I built an A
πŸ“Œ From Pixels to Plots πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-06-30 | ⏱️ Read time: 16 min read How I built an AI-powered prototype to turn images into insights

πŸ“Œ Lessons Learned After 6.5 Years Of Machine Learning πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-06-30 | ⏱️ Read time: 7 mi
πŸ“Œ Lessons Learned After 6.5 Years Of Machine Learning πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-06-30 | ⏱️ Read time: 7 min read Deep work, trends, data, and research

πŸ“Œ A Gentle Introduction to Backtracking πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-06-30 | ⏱️ Read time: 7 min read Conceptual
πŸ“Œ A Gentle Introduction to Backtracking πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-06-30 | ⏱️ Read time: 7 min read Conceptual overview and hands-on examples

πŸ“Œ Prescriptive Modeling Makes Causal Bets – Whether You Know it or Not! πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-06-30 | ⏱️ R
πŸ“Œ Prescriptive Modeling Makes Causal Bets – Whether You Know it or Not! πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-06-30 | ⏱️ Read time: 16 min read An explanation of the causal assumption implicit in prescriptive modeling and how to satisfy it.

πŸ“Œ From Reporting to Reasoning: How AI Is Rewriting the Rules of Data App Development πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025
πŸ“Œ From Reporting to Reasoning: How AI Is Rewriting the Rules of Data App Development πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-01 | ⏱️ Read time: 2 min read Explore the shift from static reports to intelligent apps with our first ebook.

πŸ“Œ Revisiting Benchmarking of Tabular Reinforcement Learning Methods πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-07-01 | ⏱️ R
πŸ“Œ Revisiting Benchmarking of Tabular Reinforcement Learning Methods πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-07-01 | ⏱️ Read time: 9 min read Introducing a modular framework and improving model performance.

πŸ“Œ Implementing IBCS rules in Power BI πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2025-07-01 | ⏱️ Read time: 12 min read Is the
πŸ“Œ Implementing IBCS rules in Power BI πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2025-07-01 | ⏱️ Read time: 12 min read Is there a way to use the out-of-the-box features of Power BI to be IBCS…

πŸ“Œ An Introduction to Remote Model Context Protocol Servers πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-01 | ⏱️ Read time: 15
πŸ“Œ An Introduction to Remote Model Context Protocol Servers πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-01 | ⏱️ Read time: 15 min read Writing, testing and using them.

πŸ“Œ STOP Building Useless ML Projects – What Actually Works πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-01 | ⏱️ Read time: 7 mi
πŸ“Œ STOP Building Useless ML Projects – What Actually Works πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-01 | ⏱️ Read time: 7 min read How to find machine learning projects that will get you hired.

πŸ“Œ How to Access NASA’s Climate Data β€” And How It’s Powering the Fight Against Climate Change Pt. 1 πŸ—‚ Category: DATA SCIENCE
πŸ“Œ How to Access NASA’s Climate Data β€” And How It’s Powering the Fight Against Climate Change Pt. 1 πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-01 | ⏱️ Read time: 11 min read From architectural design to food security.

πŸ“Œ How to Maximize Technical Events β€” NVIDIA GTC Paris 2025 πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-02 | ⏱️ Rea
πŸ“Œ How to Maximize Technical Events β€” NVIDIA GTC Paris 2025 πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-02 | ⏱️ Read time: 10 min read Learn about my experience at NVIDIA GTC Paris 25, and how you can get the…

πŸ“Œ Why We Should Focus on AI for Women πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-02 | ⏱️ Read time: 6 min read A
πŸ“Œ Why We Should Focus on AI for Women πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-02 | ⏱️ Read time: 6 min read A simulation study on gender disparities entrenched in AI.

πŸ“Œ Four AI Minds in Concert: A Deep Dive into Multimodal AI Fusion πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-02 |
πŸ“Œ Four AI Minds in Concert: A Deep Dive into Multimodal AI Fusion πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-02 | ⏱️ Read time: 36 min read Introduction: From System Architecture to Algorithmic Execution In my previous article, I explored the architectural…

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πŸ“Œ Interactive Data Exploration for Computer Vision Projects with Rerun πŸ—‚ Category: COMPUTER VISION πŸ•’ Date: 2025-07-02 | ⏱️
πŸ“Œ Interactive Data Exploration for Computer Vision Projects with Rerun πŸ—‚ Category: COMPUTER VISION πŸ•’ Date: 2025-07-02 | ⏱️ Read time: 6 min read Analyse dynamic signals in a computer vision pipeline in Python using OpenCV and Rerun

πŸ“Œ Software Engineering in the LLM Era πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-02 | ⏱️ Read time: 13 min read On
πŸ“Œ Software Engineering in the LLM Era πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-02 | ⏱️ Read time: 13 min read On growing new software engineers, even when it’s inefficient

πŸ“Œ Taking ResNet to the Next Level | ResNeXt πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2025-07-02 | ⏱️ Read time: 25 min read Under
πŸ“Œ Taking ResNet to the Next Level | ResNeXt πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2025-07-02 | ⏱️ Read time: 25 min read Understanding how ResNeXt improves upon ResNet, with a comprehensive PyTorch implementation guide