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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 40 150 subscribers, ranking 3 364 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 40 150 subscribers.

According to the latest data from 27 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 412 over the last 30 days and by 5 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 1.96%. Within the first 24 hours after publication, content typically collects 1.89% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 785 views. Within the first day, a publication typically gains 760 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
  • 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 28 June, 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.

40 150
Subscribers
+524 hours
+1067 days
+41230 days
Posts Archive
πŸ“Œ Roadmap to Becoming a Data Scientist, Part 4: Advanced Machine Learning πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-02-14 | ⏱️
πŸ“Œ Roadmap to Becoming a Data Scientist, Part 4: Advanced Machine Learning πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-02-14 | ⏱️ Read time: 15 min read Introduction Data science is undoubtedly one of the most fascinating fields today. Following significant breakthroughs in…

πŸ“Œ On-Device Machine Learning in Spatial Computing πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-02-17 | ⏱️ Read time: 18 min r
πŸ“Œ On-Device Machine Learning in Spatial Computing πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-02-17 | ⏱️ Read time: 18 min read The landscape of computing is undergoing a profound transformation with the emergence of spatial computing…

Mining made simple No need to understand DeFi or charts. Buy a miner β†’ get coins β†’ withdraw profit. It’s the easiest way to e
Mining made simple No need to understand DeFi or charts. Buy a miner β†’ get coins β†’ withdraw profit. It’s the easiest way to enter blockchain. ⚑️ Try it now #ad InsideAds

πŸ“Œ Deep Dive into Anthropic’s Sparse Autoencoders by Hand πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-05-31 | ⏱️ Read ti
πŸ“Œ Deep Dive into Anthropic’s Sparse Autoencoders by Hand πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-05-31 | ⏱️ Read time: 12 min read Explore the concepts behind the interpretability quest for LLMs

πŸ“Œ A Deep Dive into In-Context Learning πŸ—‚ Category: NATURAL LANGUAGE PROCESSING πŸ•’ Date: 2024-05-31 | ⏱️ Read time: 11 min r
πŸ“Œ A Deep Dive into In-Context Learning πŸ—‚ Category: NATURAL LANGUAGE PROCESSING πŸ•’ Date: 2024-05-31 | ⏱️ Read time: 11 min read Stepping out of the β€œcomfort zone” – part 2/3 of a deep-dive into domain adaptation…

πŸ“Œ YOLO – Intuitively and Exhaustively Explained πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-05-31 | ⏱️ Read time: 31 min rea
πŸ“Œ YOLO – Intuitively and Exhaustively Explained πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-05-31 | ⏱️ Read time: 31 min read The genesis of the most widely used object detection models.

πŸ“Œ AI Use Cases are Fundamentally Different πŸ—‚ Category: ROBOTICS πŸ•’ Date: 2024-05-31 | ⏱️ Read time: 9 min read How to find
πŸ“Œ AI Use Cases are Fundamentally Different πŸ—‚ Category: ROBOTICS πŸ•’ Date: 2024-05-31 | ⏱️ Read time: 9 min read How to find unique use cases for AI and places where moderate AI performance is…

πŸ“Œ Why You Don’t Need JS to Make 3D plots πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-01 | ⏱️ Read time: 6 min read Visualizin
πŸ“Œ Why You Don’t Need JS to Make 3D plots πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-01 | ⏱️ Read time: 6 min read Visualizing crime geodata in python

πŸ“Œ Performance Insights from Sigma Rule Detections in Spark Streaming πŸ—‚ Category: CYBERSECURITY πŸ•’ Date: 2024-06-01 | ⏱️ Rea
πŸ“Œ Performance Insights from Sigma Rule Detections in Spark Streaming πŸ—‚ Category: CYBERSECURITY πŸ•’ Date: 2024-06-01 | ⏱️ Read time: 13 min read Utilizing Sigma rules for anomaly detection in cybersecurity logs: A study on performance optimization

πŸ“Œ PRISM-Rules in Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-02 | ⏱️ Read time: 14 min read A simple python rules-indu
πŸ“Œ PRISM-Rules in Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-02 | ⏱️ Read time: 14 min read A simple python rules-induction system

πŸ“Œ How I Use ChatGPT As A Data Scientist πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-06-02 | ⏱️ Read time: 8 min read
πŸ“Œ How I Use ChatGPT As A Data Scientist πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-06-02 | ⏱️ Read time: 8 min read How ChatGPT improved my productivity as a data scientist

πŸ“Œ Comparing Country Sizes with GeoPandas πŸ—‚ Category: πŸ•’ Date: 2024-06-02 | ⏱️ Read time: 14 min read How to project, shift,
πŸ“Œ Comparing Country Sizes with GeoPandas πŸ—‚ Category: πŸ•’ Date: 2024-06-02 | ⏱️ Read time: 14 min read How to project, shift, and rotate geospatial data

πŸ“Œ Measuring The Intrinsic Causal Influence Of Your Marketing Campaigns πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-02 | ⏱️ Re
πŸ“Œ Measuring The Intrinsic Causal Influence Of Your Marketing Campaigns πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-02 | ⏱️ Read time: 11 min read Causal AI, exploring the integration of causal reasoning into machine learning

πŸ“Œ Linear Attention Is All You Need πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-06-02 | ⏱️ Read time: 10 min read Self-a
πŸ“Œ Linear Attention Is All You Need πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-06-02 | ⏱️ Read time: 10 min read Self-attention at a fraction of the cost?

πŸ“Œ ML Engineering 101: A Thorough Explanation of The Error β€œDataLoader worker (pid(s) xxx) exited… πŸ—‚ Category: DATA SCIENCE
πŸ“Œ ML Engineering 101: A Thorough Explanation of The Error β€œDataLoader worker (pid(s) xxx) exited… πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-03 | ⏱️ Read time: 6 min read A deep dive into PyTorch DataLoader with Multiprocessing

πŸ“Œ Optimizing Memory Consumption for Data Analytics Using Python – From 400 to 0.1 πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06
πŸ“Œ Optimizing Memory Consumption for Data Analytics Using Python – From 400 to 0.1 πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-03 | ⏱️ Read time: 9 min read Reducing the memory consumption of your code means reducing hardware requirements

πŸ“Œ Bit-LoRA as an application of BitNet and 1.58 bit neural network technologies πŸ—‚ Category: πŸ•’ Date: 2024-06-03 | ⏱️ Read t
πŸ“Œ Bit-LoRA as an application of BitNet and 1.58 bit neural network technologies πŸ—‚ Category: πŸ•’ Date: 2024-06-03 | ⏱️ Read time: 15 min read Abstract: applying ~1bit transformer technology to LoRA adapters allows us to reach comparable performance with…

πŸ“Œ The Trap of Sprints: Don’t Be Like Scarlett O’Hara. Think Today! πŸ—‚ Category: AGILE πŸ•’ Date: 2024-06-03 | ⏱️ Read time: 11
πŸ“Œ The Trap of Sprints: Don’t Be Like Scarlett O’Hara. Think Today! πŸ—‚ Category: AGILE πŸ•’ Date: 2024-06-03 | ⏱️ Read time: 11 min read Why data scientists should prioritize communication and flexibility in agile projects

πŸ“Œ A Deep Dive into Fine-Tuning πŸ—‚ Category: NATURAL LANGUAGE PROCESSING πŸ•’ Date: 2024-06-03 | ⏱️ Read time: 30 min read Step
πŸ“Œ A Deep Dive into Fine-Tuning πŸ—‚ Category: NATURAL LANGUAGE PROCESSING πŸ•’ Date: 2024-06-03 | ⏱️ Read time: 30 min read Stepping out of the β€œcomfort zone” – part 3/3 of a deep-dive into domain adaptation…

πŸ“Œ The Meaning of Explainability for AI πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-06-04 | ⏱️ Read time: 10 min read
πŸ“Œ The Meaning of Explainability for AI πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-06-04 | ⏱️ Read time: 10 min read Do we still care about how our machine learning does what it does?