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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 40 310 subscribers, ranking 3 332 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 40 310 subscribers.

According to the latest data from 09 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 378 over the last 30 days and by 30 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.23%. Within the first 24 hours after publication, content typically collects 1.95% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 897 views. Within the first day, a publication typically gains 788 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 10 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.

40 310
Subscribers
+3024 hours
+1067 days
+37830 days
Posts Archive
πŸ“Œ Nine Pico PIO Wats with MicroPython (Part 2) πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-01-28 | ⏱️ Read time: 16 min read Rasp
πŸ“Œ Nine Pico PIO Wats with MicroPython (Part 2) πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-01-28 | ⏱️ Read time: 16 min read Raspberry Pi programmable IO pitfalls illustrated with a musical example

πŸ“Œ The Three Phases of Learning Machine Learning πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-28 | ⏱️ Read time: 7 min read Par
πŸ“Œ The Three Phases of Learning Machine Learning πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-28 | ⏱️ Read time: 7 min read Part One: The beginner phase

πŸ“Œ Battle of the Ducks πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2025-01-28 | ⏱️ Read time: 13 min read DuckDB vs Fireducks: the
πŸ“Œ Battle of the Ducks πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2025-01-28 | ⏱️ Read time: 13 min read DuckDB vs Fireducks: the ultimate throwdown

πŸ“Œ How to do Date calculations in DAX πŸ—‚ Category: πŸ•’ Date: 2025-01-28 | ⏱️ Read time: 6 min read Moving back and forth in ti
πŸ“Œ How to do Date calculations in DAX πŸ—‚ Category: πŸ•’ Date: 2025-01-28 | ⏱️ Read time: 6 min read Moving back and forth in time is a common task for Time Intelligence in DAX.…

πŸ“Œ How GenAI Tools Have Changed My Work as a Data Scientist πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-28 | ⏱️ Rea
πŸ“Œ How GenAI Tools Have Changed My Work as a Data Scientist πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-28 | ⏱️ Read time: 11 min read An overview of the 4 use cases and 6 GenAI tools I use

πŸ“Œ Exploring DeepSeek’s R1 Training Process πŸ—‚ Category: πŸ•’ Date: 2025-01-29 | ⏱️ Read time: 11 min read Open-Source Intellig
πŸ“Œ Exploring DeepSeek’s R1 Training Process πŸ—‚ Category: πŸ•’ Date: 2025-01-29 | ⏱️ Read time: 11 min read Open-Source Intelligence on Par with Proprietary Models

πŸ“Œ Prompting Vision Language Models πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-01-29 | ⏱️ Read time: 21 min read Explor
πŸ“Œ Prompting Vision Language Models πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-01-29 | ⏱️ Read time: 21 min read Exploring techniques to prompt VLMs

πŸ“Œ NLP Illustrated, Part 3: Word2Vec πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-29 | ⏱️ Read time: 13 min read An exhaustive
πŸ“Œ NLP Illustrated, Part 3: Word2Vec πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-29 | ⏱️ Read time: 13 min read An exhaustive and illustrated guide to Word2Vec with code!

πŸ“Œ AI Ethics for the Everyday User – Why Should You Care? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-29 | ⏱️ Read
πŸ“Œ AI Ethics for the Everyday User – Why Should You Care? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-29 | ⏱️ Read time: 15 min read A beginner’s guide to understanding the importance of ethics in artificial intelligence

πŸ“Œ Bite-Size Data Science: Falling for the Gambler’s Fallacy πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-30 | ⏱️ Read time: 12
πŸ“Œ Bite-Size Data Science: Falling for the Gambler’s Fallacy πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-30 | ⏱️ Read time: 12 min read Where the gambler’s fallacy shows up in data science and what to do about it

πŸ“Œ Ridge Regression: A Robust Path to Reliable Predictions πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-30 | ⏱️ Read time: 11 m
πŸ“Œ Ridge Regression: A Robust Path to Reliable Predictions πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-30 | ⏱️ Read time: 11 min read Learn how regularization reduces overfitting and improves model stability in linear regression.

πŸ“Œ A Visual Guide to How Diffusion Models Work πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-02-06 | ⏱️ Read time: 26 min read
πŸ“Œ A Visual Guide to How Diffusion Models Work πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-02-06 | ⏱️ Read time: 26 min read This article is aimed at those who want to understand exactly how diffusion models work,…

πŸ“Œ Introduction to Minimum Cost Flow Optimization in Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-02-06 | ⏱️ Read time: 21
πŸ“Œ Introduction to Minimum Cost Flow Optimization in Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-02-06 | ⏱️ Read time: 21 min read Minimum cost flow optimization minimizes the cost of moving flow through a network of nodes…

πŸ“Œ Efficient Metric Collection in PyTorch: Avoiding the Performance Pitfalls of TorchMetrics πŸ—‚ Category: MACHINE LEARNING πŸ•’
πŸ“Œ Efficient Metric Collection in PyTorch: Avoiding the Performance Pitfalls of TorchMetrics πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-02-06 | ⏱️ Read time: 13 min read Metric collection is an essential part of every machine learning project, enabling us to track…

πŸ“Œ How to Create Network Graph Visualizations in Microsoft PowerBI πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2025-02-07 | ⏱️ R
πŸ“Œ How to Create Network Graph Visualizations in Microsoft PowerBI πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2025-02-07 | ⏱️ Read time: 6 min read Microsoft PowerBI is a one of the most popular business intelligence (BI) tools, and while…

πŸ“Œ A Comprehensive Guide to LLM Temperature πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-02-07 | ⏱️ Read time: 8 min read
πŸ“Œ A Comprehensive Guide to LLM Temperature πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-02-07 | ⏱️ Read time: 8 min read While building my own LLM-based application, I found many prompt engineering guides, but few equivalent…

πŸ“Œ The Method of Moments Estimator for Gaussian Mixture Models πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-02-07 | ⏱️ Read time:
πŸ“Œ The Method of Moments Estimator for Gaussian Mixture Models πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-02-07 | ⏱️ Read time: 8 min read Audio processing is one of the most important application domains of digital signal processing (DSP)…

πŸ“Œ Synthetic Data Generation with LLMs πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-02-07 | ⏱️ Read time: 9 min read Popu
πŸ“Œ Synthetic Data Generation with LLMs πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-02-07 | ⏱️ Read time: 9 min read Popularity of RAG Over the past two years while working with financial firms, I’ve observed…

πŸ“Œ I Tried Making my Own (Bad) LLM Benchmark to Cheat in Escape Rooms πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-02-07 | ⏱️
πŸ“Œ I Tried Making my Own (Bad) LLM Benchmark to Cheat in Escape Rooms πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-02-07 | ⏱️ Read time: 20 min read Recently, DeepSeek announced their latest model, R1, and article after article came out praising its…

πŸ“Œ Triangle Forecasting: Why Traditional Impact Estimates Are Inflated (And How to Fix Them) πŸ—‚ Category: DATA SCIENCE πŸ•’ Dat
πŸ“Œ Triangle Forecasting: Why Traditional Impact Estimates Are Inflated (And How to Fix Them) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-02-07 | ⏱️ Read time: 7 min read Accurate impact estimations can make or break your business case. Yet, despite its importance, most…