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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 TDS Authors Can Now Edit Their Published Articles πŸ—‚ Category: WRITING πŸ•’ Date: 2025-07-18 | ⏱️ Read time: 3 min read One
πŸ“Œ TDS Authors Can Now Edit Their Published Articles πŸ—‚ Category: WRITING πŸ•’ Date: 2025-07-18 | ⏱️ Read time: 3 min read One of our guiding principles as a publication is that authors’ work remains theirs. This…

πŸ“Œ From Reactive to Predictive: Forecasting Network Congestion with Machine Learning and INT πŸ—‚ Category: MACHINE LEARNING πŸ•’
πŸ“Œ From Reactive to Predictive: Forecasting Network Congestion with Machine Learning and INT πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-07-18 | ⏱️ Read time: 7 min read Learn how machine learning can predict network congestion before it happens

πŸ“Œ Gain a Better Understanding of Computer Vision: Dynamic SOLO (SOLOv2) with TensorFlow πŸ—‚ Category: COMPUTER VISION πŸ•’ Date
πŸ“Œ Gain a Better Understanding of Computer Vision: Dynamic SOLO (SOLOv2) with TensorFlow πŸ—‚ Category: COMPUTER VISION πŸ•’ Date: 2025-07-18 | ⏱️ Read time: 16 min read A practical approach to instance segmentation using SOLOv2 and TensorFlow

πŸ“Œ The Hidden Trap of Fixed and Random Effects πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-18 | ⏱️ Read time: 6 min read My le
πŸ“Œ The Hidden Trap of Fixed and Random Effects πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-18 | ⏱️ Read time: 6 min read My lesson of how blindly over-controlling for noise can erase the effects you are measuring

πŸ“Œ Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 2) πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-07-18 | ⏱️ Re
πŸ“Œ Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 2) πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-07-18 | ⏱️ Read time: 19 min read Let’s observe the matter on the atomic level

πŸ“Œ How to Create an LLM Judge That Aligns with Human Labels πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-21 | ⏱️ Read
πŸ“Œ How to Create an LLM Judge That Aligns with Human Labels πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-21 | ⏱️ Read time: 14 min read A hands-on guide to building and validating LLM evaluators

πŸ“Œ Three Career Tips For Gen-Z Data Professionals πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-21 | ⏱️ Read time: 10 min read U
πŸ“Œ Three Career Tips For Gen-Z Data Professionals πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-21 | ⏱️ Read time: 10 min read Unsolicited pieces of advice on navigating early career challenges

πŸ“Œ Advanced Topic Modeling with LLMs πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-21 | ⏱️ Read time: 12 min read A dee
πŸ“Œ Advanced Topic Modeling with LLMs πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-21 | ⏱️ Read time: 12 min read A deep dive into topic modeling by leveraging representation models and generative AI with BERTopic

πŸ“Œ Hands‑On with Agents SDK: Your First API‑Calling Agent πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-21 | ⏱️ Read
πŸ“Œ Hands‑On with Agents SDK: Your First API‑Calling Agent πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-21 | ⏱️ Read time: 16 min read A practical, beginner‑friendly guide to building an AI weather assistant with Python, OpenAI Agents SDK,…

πŸ“Œ I Analysed 25,000 Hotel Names and Found Four Surprising Truths πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-21 | ⏱️ Read tim
πŸ“Œ I Analysed 25,000 Hotel Names and Found Four Surprising Truths πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-21 | ⏱️ Read time: 10 min read Why are there so many hotels named after cities they are not in? Follow along…

πŸ“Œ How To Significantly Enhance LLMs by Leveraging Context Engineering πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-21
πŸ“Œ How To Significantly Enhance LLMs by Leveraging Context Engineering πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-21 | ⏱️ Read time: 11 min read The benefits and practical aspects of context engineering for LLMs

πŸ“Œ When LLMs Try to Reason: Experiments in Text and Vision-Based Abstraction πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025
πŸ“Œ When LLMs Try to Reason: Experiments in Text and Vision-Based Abstraction πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-22 | ⏱️ Read time: 21 min read Can large language models learn to reason abstractly from just a few examples? In this…

πŸ“Œ Understanding Matrices | Part 3: Matrix Transpose πŸ—‚ Category: MATH πŸ•’ Date: 2025-07-22 | ⏱️ Read time: 13 min read Visual
πŸ“Œ Understanding Matrices | Part 3: Matrix Transpose πŸ—‚ Category: MATH πŸ•’ Date: 2025-07-22 | ⏱️ Read time: 13 min read Visualizing matrix transposition, to make sense of transpose-related formulas.

πŸ“Œ What Optimization Terminologies for Linear Programming Really Mean πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-22 | ⏱️ Read
πŸ“Œ What Optimization Terminologies for Linear Programming Really Mean πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-22 | ⏱️ Read time: 11 min read Understanding the duality of optimization problem, primal to dual conversion, and the optimality conditions for…

πŸ“Œ From Rules to Relationships: How Machines Are Learning to Understand Each Other πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 202
πŸ“Œ From Rules to Relationships: How Machines Are Learning to Understand Each Other πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-07-22 | ⏱️ Read time: 6 min read Using knowledge graphs to handle the unexpected in semantic communication

πŸ“Œ A Well-Designed Experiment Can Teach You More Than a Time Machine! πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-22 | ⏱️ Read
πŸ“Œ A Well-Designed Experiment Can Teach You More Than a Time Machine! πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-22 | ⏱️ Read time: 7 min read How experimentation is more powerful than knowing counterfactuals

πŸ“Œ Things I Wish I Had Known Before Starting ML πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-07-22 | ⏱️ Read time: 9 min read
πŸ“Œ Things I Wish I Had Known Before Starting ML πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-07-22 | ⏱️ Read time: 9 min read Part 1: Data, Sales Pitches, Bugs, and Breakthroughs

πŸ“Œ NumPy API on a GPU? πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-07-22 | ⏱️ Read time: 17 min read It’s here already from Nvidia
πŸ“Œ NumPy API on a GPU? πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-07-22 | ⏱️ Read time: 17 min read It’s here already from Nvidia and it’s called cuNumeric.

πŸ“Œ Torchvista: Building an Interactive Pytorch Visualization Package for Notebooks πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Da
πŸ“Œ Torchvista: Building an Interactive Pytorch Visualization Package for Notebooks πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-23 | ⏱️ Read time: 11 min read Building a tool to interactively visualize the forward pass of any Pytorch model from within…

πŸ“Œ How Not to Mislead with Your Data-Driven Story πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-23 | ⏱️ Read time: 22 min read D
πŸ“Œ How Not to Mislead with Your Data-Driven Story πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-23 | ⏱️ Read time: 22 min read Data storytelling can enlightenβ€”but it can also deceive. When persuasive narratives meet biased framing, cherry-picked…