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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 40 273 subscribers, ranking 3 347 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 40 273 subscribers.

According to the latest data from 07 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 352 over the last 30 days and by 21 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 1.88% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 896 views. Within the first day, a publication typically gains 758 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 08 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.

40 273
Subscribers
+2124 hours
+957 days
+35230 days
Posts Archive
πŸ“Œ Learning ML or Learning About Learning ML? πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-03 | ⏱️ Read time: 4 min read Pragma
πŸ“Œ Learning ML or Learning About Learning ML? πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-03 | ⏱️ Read time: 4 min read Pragmatism versus (over-)planning

I spent months trying every way to grow my Telegram channel. Nothing worked… until I found this secret tool. It finds adverti
I spent months trying every way to grow my Telegram channel. Nothing worked… until I found this secret tool. It finds advertisers and subscribers for you β€” and even pays you! Now my channel is earning while I sleep. Want the same results? Try it here #ad InsideAds.

πŸ“Œ Statistical Learnability of Strategic Linear Classifiers: A Proof Walkthrough πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-
πŸ“Œ Statistical Learnability of Strategic Linear Classifiers: A Proof Walkthrough πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-01-08 | ⏱️ Read time: 19 min read With the help of an intricate geometric construction, we can prove that instance-wise cost functions…

πŸ“Œ Advanced SQL Techniques for Unstructured Data Handling πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-08 | ⏱️ Read time: 7 min
πŸ“Œ Advanced SQL Techniques for Unstructured Data Handling πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-08 | ⏱️ Read time: 7 min read Everything you need to know to get started with text mining

πŸ“Œ Missing Data in Time-Series? Machine Learning Techniques (Part 2) πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-01-08 | ⏱️ R
πŸ“Œ Missing Data in Time-Series? Machine Learning Techniques (Part 2) πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-01-08 | ⏱️ Read time: 12 min read Using Clustering Algorithms to Handle Missing Time-Series Data

πŸ“Œ Speed Up PyTorch With Custom Kernels. But It Gets Progressively Darker πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-
πŸ“Œ Speed Up PyTorch With Custom Kernels. But It Gets Progressively Darker πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-09 | ⏱️ Read time: 5 min read We’ll begin with torch.compile, move on to writing a custom Triton kernel, and finally dive…

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Are you tired of missing out on real profits? Unlock FREE trading signals from a PRO with 7+ years of wins! πŸ’Ή Over 85% accuracy, up to 4000+ pips monthlyβ€”all for FREE. Join the winners who receive instant trade alerts for Gold, Forex, and Crypto. Don’t wait while others cash inβ€”get your free signals now! Profitable trades are just a click away. #ad InsideAds

πŸ“Œ Jingle Bells and Statistical Tests πŸ—‚ Category: STATISTICS πŸ•’ Date: 2024-12-25 | ⏱️ Read time: 8 min read Data Types, Hypo
πŸ“Œ Jingle Bells and Statistical Tests πŸ—‚ Category: STATISTICS πŸ•’ Date: 2024-12-25 | ⏱️ Read time: 8 min read Data Types, Hypotheses and Statistical Tests That Fit Them with Festive Christmas Market Examples

πŸ“Œ Understanding When and How to Implement FastAPI Middleware (Examples and Use Cases) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 202
πŸ“Œ Understanding When and How to Implement FastAPI Middleware (Examples and Use Cases) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-25 | ⏱️ Read time: 4 min read Supercharge Your FastAPI with Middleware: Practical Use Cases and Examples

πŸ“Œ Training LLM, from Scratch, in Rust πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-12-26 | ⏱️ Read time: 16 min read In this
πŸ“Œ Training LLM, from Scratch, in Rust πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-12-26 | ⏱️ Read time: 16 min read In this companion article, I’ll show my implementation for training from scratch a GPT-like model,…

πŸ“Œ Calculating a linear extrapolation (or Trend) in DAX πŸ—‚ Category: πŸ•’ Date: 2024-12-26 | ⏱️ Read time: 10 min read In DAX t
πŸ“Œ Calculating a linear extrapolation (or Trend) in DAX πŸ—‚ Category: πŸ•’ Date: 2024-12-26 | ⏱️ Read time: 10 min read In DAX there is no built-in method to calculate a Trend. Therefore we must do…

πŸ“Œ Your Company Needs Small Language Models πŸ—‚ Category: CHATGPT πŸ•’ Date: 2024-12-26 | ⏱️ Read time: 15 min read When special
πŸ“Œ Your Company Needs Small Language Models πŸ—‚ Category: CHATGPT πŸ•’ Date: 2024-12-26 | ⏱️ Read time: 15 min read When specialized models outperform general-purpose models

πŸ“Œ Linearizing Attention πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-12-26 | ⏱️ Read time: 8 min read Breaking the Quadr
πŸ“Œ Linearizing Attention πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-12-26 | ⏱️ Read time: 8 min read Breaking the Quadratic Barrier: Modern Alternatives to Softmax Attention

πŸ“Œ How Neural Networks Learn: A Probabilistic Viewpoint πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-12-26 | ⏱️ Read time: 8 m
πŸ“Œ How Neural Networks Learn: A Probabilistic Viewpoint πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-12-26 | ⏱️ Read time: 8 min read Understanding Loss Functions in Training Neural Networks

πŸ“Œ Track Computer Vision Experiments with MLflow πŸ—‚ Category: πŸ•’ Date: 2024-12-26 | ⏱️ Read time: 10 min read Discover how to
πŸ“Œ Track Computer Vision Experiments with MLflow πŸ—‚ Category: πŸ•’ Date: 2024-12-26 | ⏱️ Read time: 10 min read Discover how to set up an efficient MLflow environment to track your experiments, compare and…

πŸ“Œ Lessons from COVID-19: Why Probability Distributions Matter πŸ—‚ Category: COVID-19 πŸ•’ Date: 2024-12-27 | ⏱️ Read time: 13 m
πŸ“Œ Lessons from COVID-19: Why Probability Distributions Matter πŸ—‚ Category: COVID-19 πŸ•’ Date: 2024-12-27 | ⏱️ Read time: 13 min read Understanding Distributions with Extremes: Probability for Data Science Series (END)

πŸ“Œ Understanding the Optimization Process Pipeline in Linear Programming πŸ—‚ Category: πŸ•’ Date: 2024-12-27 | ⏱️ Read time: 8 m
πŸ“Œ Understanding the Optimization Process Pipeline in Linear Programming πŸ—‚ Category: πŸ•’ Date: 2024-12-27 | ⏱️ Read time: 8 min read An introduction to the backend and frontend processes in linear programming, including the mathematical programming…

πŸ“Œ Master Bots Before Starting with AI Agents: Simple Steps to Create a Mastodon Bot with Python πŸ—‚ Category: PROGRAMMING πŸ•’
πŸ“Œ Master Bots Before Starting with AI Agents: Simple Steps to Create a Mastodon Bot with Python πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2024-12-27 | ⏱️ Read time: 13 min read I recently published a post on Mastodon that was shared by six other accounts within…

πŸ“Œ Measuring Cross-Product Adoption Using dbt_set_similarity πŸ—‚ Category: SQL πŸ•’ Date: 2024-12-28 | ⏱️ Read time: 5 min read
πŸ“Œ Measuring Cross-Product Adoption Using dbt_set_similarity πŸ—‚ Category: SQL πŸ•’ Date: 2024-12-28 | ⏱️ Read time: 5 min read Enhancing cross-product insights within dbt workflows

πŸ“Œ Deep Dive into Multithreading, Multiprocessing, and Asyncio πŸ—‚ Category: πŸ•’ Date: 2024-12-28 | ⏱️ Read time: 10 min read H
πŸ“Œ Deep Dive into Multithreading, Multiprocessing, and Asyncio πŸ—‚ Category: πŸ•’ Date: 2024-12-28 | ⏱️ Read time: 10 min read How to choose the right concurrency model