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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 40 202 subscribers, ranking 3 365 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 40 202 subscribers.

According to the latest data from 02 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 343 over the last 30 days and by 10 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 1.99%. Within the first 24 hours after publication, content typically collects 2.28% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 800 views. Within the first day, a publication typically gains 915 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 03 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.

40 202
Subscribers
+1024 hours
+837 days
+34330 days
Posts Archive
πŸ“Œ Python QuickStart for People Learning AI πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-08 | ⏱️ Read time: 15 min read A begin
πŸ“Œ Python QuickStart for People Learning AI πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-08 | ⏱️ Read time: 15 min read A beginner-friendly guide

πŸ“Œ Galactic Distances πŸ—‚ Category: πŸ•’ Date: 2024-09-08 | ⏱️ Read time: 18 min read How Far Are We from Alien Civilizations? (
πŸ“Œ Galactic Distances πŸ—‚ Category: πŸ•’ Date: 2024-09-08 | ⏱️ Read time: 18 min read How Far Are We from Alien Civilizations? (Part 4 of the Drake Equation Series)

πŸ“Œ Are We Alone? πŸ—‚ Category: SCIENCE AND TECHNOLOGY πŸ•’ Date: 2024-09-08 | ⏱️ Read time: 12 min read The Real Odds of Encount
πŸ“Œ Are We Alone? πŸ—‚ Category: SCIENCE AND TECHNOLOGY πŸ•’ Date: 2024-09-08 | ⏱️ Read time: 12 min read The Real Odds of Encountering Alien Life (Part 5 of the Drake Equation Series)

πŸ“Œ Automate Video Chaptering with LLMs and TF-IDF πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-09-09 | ⏱️ Read time: 14 m
πŸ“Œ Automate Video Chaptering with LLMs and TF-IDF πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-09-09 | ⏱️ Read time: 14 min read Transform raw transcripts into well-structured documents

πŸ“Œ Benchmarking Hallucination Detection Methods in RAG πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-09-09 | ⏱️ Read time:
πŸ“Œ Benchmarking Hallucination Detection Methods in RAG πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-09-09 | ⏱️ Read time: 11 min read Evaluating methods to enhance reliability in LLM-generated responses.

πŸ“Œ Does Semi-Supervised Learning Help to Train Better Models? πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-09-09 | ⏱️ Read tim
πŸ“Œ Does Semi-Supervised Learning Help to Train Better Models? πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-09-09 | ⏱️ Read time: 8 min read Evaluating how semi-supervised learning can leverage unlabeled data

πŸ“Œ Is Multi-Collinearity Destroying Your Causal Inferences In Marketing Mix Modelling? πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 202
πŸ“Œ Is Multi-Collinearity Destroying Your Causal Inferences In Marketing Mix Modelling? πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 18 min read Causal AI, exploring the integration of causal reasoning into machine learning

πŸ“Œ MobileNetV2 Paper Walkthrough: The Smarter Tiny Giant πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2025-10-03 | ⏱️ Read time: 28 mi
πŸ“Œ MobileNetV2 Paper Walkthrough: The Smarter Tiny Giant πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2025-10-03 | ⏱️ Read time: 28 min read Understanding and implementing MobileNetV2 with PyTorchβ€Š β€” the next generation of MobileNetV1

πŸ“Œ Build a Data Dashboard Using HTML, CSS, and JavaScript πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-10-03 | ⏱️ Read time: 14 min
πŸ“Œ Build a Data Dashboard Using HTML, CSS, and JavaScript πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-10-03 | ⏱️ Read time: 14 min read A framework-free guide for Python programmers

πŸ“Œ Introducing NumPy, Part 3: Manipulating Arrays πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-15 | ⏱️ Read time: 7 min read Sh
πŸ“Œ Introducing NumPy, Part 3: Manipulating Arrays πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-15 | ⏱️ Read time: 7 min read Shaping, transposing, joining, and splitting arrays

soon

I used to think trading was all about luck… until I saw what happens when you apply actual discipline. My P&L chart never loo
I used to think trading was all about luck… until I saw what happens when you apply actual discipline. My P&L chart never looked the same again. If you want to discover the real secrets no one tells you β€” check here: see for yourself #ad InsideAds

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🌍 Work Abroad for Skilled Construction Workers! Salary: $450–700 per month βœ… Free accommodation βœ… Free meals βœ… Official 1-year work contract πŸ“Œ Open positions: β€’ Tilers β€’ Painters / Plasterers β€’ Bricklayers β€’ Facade Workers β€’ Plumbers β€’ Electricians πŸ’‘ Experience required! πŸ“² Apply now #ad InsideAds

Want to grab insane deals before everyone else? Why pay more for your everyday shopping when you can score exclusive discount
Want to grab insane deals before everyone else? Why pay more for your everyday shopping when you can score exclusive discounts from Amazon, Flipkart, Zomato & more? Stop missing out on loot dealsβ€”discover today’s best offers right here. Join now and save big on every purchase! #ad InsideAds

Nobody believed I could boost my speed with just one hidden tech trickβ€”until I did. Now, my Android feels like new and my iPh
Nobody believed I could boost my speed with just one hidden tech trickβ€”until I did. Now, my Android feels like new and my iPhone unlocks features I never expected. The secret? Find out before everyone else β€” only revealed here! #ad InsideAds

No one tells you this, but sometimes you need to disappear for 24 hours to finally find yourself. Could you survive it? Every
No one tells you this, but sometimes you need to disappear for 24 hours to finally find yourself. Could you survive it? Everyone talks about problems and pain… but what if you just walked awayβ€”for a day? Find out the answer here β€” dare to try? #ad InsideAds

πŸ“Œ Key Insights for Teaching AI Agents to Remember πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 2
πŸ“Œ Key Insights for Teaching AI Agents to Remember πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 20 min read Recommendations on building robust memory capabilities based on experimentation with Autogen’s β€œTeachable Agents”

πŸ“Œ The Art of Asking Questions for Engineers πŸ—‚ Category: BUSINESS πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 6 min read A Guideline
πŸ“Œ The Art of Asking Questions for Engineers πŸ—‚ Category: BUSINESS πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 6 min read A Guideline for Asking Impactful Questions

πŸ“Œ Practical Introduction to Polars πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 13 min read Hands-on guide
πŸ“Œ Practical Introduction to Polars πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 13 min read Hands-on guide with side-by-side examples in Pandas

πŸ“Œ Logistic Regression, Explained: A Visual Guide with Code Examples for Beginners πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09
πŸ“Œ Logistic Regression, Explained: A Visual Guide with Code Examples for Beginners πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 9 min read Finding the perfect weights to fit the data in