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Machine Learning

Machine Learning

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Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

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📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 334 subscribers, ranking 3 331 in the Technologies & Applications category and 225 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 334 subscribers.

According to the latest data from 10 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 383 over the last 30 days and by 25 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.35%. 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 948 views. Within the first day, a publication typically gains 786 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.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
Real Machine Learning — 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 11 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.

40 334
Subscribers
+2524 hours
+1227 days
+38330 days
Posts Archive
📌 Will You Spot the Leaks? A Data Science Challenge 🗂 Category: DATA SCIENCE 🕒 Date: 2025-05-12 | ⏱️ Read time: 8 min read
📌 Will You Spot the Leaks? A Data Science Challenge 🗂 Category: DATA SCIENCE 🕒 Date: 2025-05-12 | ⏱️ Read time: 8 min read When models fly too high: A perilous journey through data leakage

📌 Running Python Programs in Your Browser 🗂 Category: PROGRAMMING 🕒 Date: 2025-05-12 | ⏱️ Read time: 17 min read Using Pyo
📌 Running Python Programs in Your Browser 🗂 Category: PROGRAMMING 🕒 Date: 2025-05-12 | ⏱️ Read time: 17 min read Using Pyodide and Webassembly

📌 Pause Your ML Pipelines for Human Review Using AWS Step Functions + Slack 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-05-1
📌 Pause Your ML Pipelines for Human Review Using AWS Step Functions + Slack 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-05-12 | ⏱️ Read time: 7 min read Build trust into your machine learning pipelines by inserting fast, secure human checks.

📌 The Westworld Blunder 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-12 | ⏱️ Read time: 16 min read Giving artifici
📌 The Westworld Blunder 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-12 | ⏱️ Read time: 16 min read Giving artificial minds the appearance of suffering without the awareness that it’s just a performance…

📌 How I Finally Understood MCP — and Got It Working in Real Life 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-05-12 | ⏱️
📌 How I Finally Understood MCP — and Got It Working in Real Life 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-05-12 | ⏱️ Read time: 28 min read The guide I needed when I had no idea why anyone would build an MCP…

📌 Empowering LLMs to Think Deeper by Erasing Thoughts 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-05-12 | ⏱️ Read time:
📌 Empowering LLMs to Think Deeper by Erasing Thoughts 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-05-12 | ⏱️ Read time: 11 min read Introduction Recent large language models (LLMs) — such as OpenAI’s o1/o3, DeepSeek’s R1 and Anthropic’s…

📌 TDS Authors Can Now Receive Payments Via Stripe 🗂 Category: WRITING 🕒 Date: 2025-05-13 | ⏱️ Read time: 2 min read The Au
📌 TDS Authors Can Now Receive Payments Via Stripe 🗂 Category: WRITING 🕒 Date: 2025-05-13 | ⏱️ Read time: 2 min read The Author Payment Program just became a lot more streamlined

📌 Rethinking the Environmental Costs of Training AI — Why We Should Look Beyond Hardware 🗂 Category: ARTIFICIAL INTELLIGENC
📌 Rethinking the Environmental Costs of Training AI — Why We Should Look Beyond Hardware 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-13 | ⏱️ Read time: 19 min read A statistical analysis of what drives energy, water, and carbon consumption in AI training —…

📌 Non-Parametric Density Estimation: Theory and Applications 🗂 Category: STATISTICS 🕒 Date: 2025-05-13 | ⏱️ Read time: 26
📌 Non-Parametric Density Estimation: Theory and Applications 🗂 Category: STATISTICS 🕒 Date: 2025-05-13 | ⏱️ Read time: 26 min read A theoretical and practical introduction to non-parametric density estimation.

📌 Get Started with Rust: Installation and Your First CLI Tool – A Beginner’s Guide 🗂 Category: PROGRAMMING 🕒 Date: 2025-05
📌 Get Started with Rust: Installation and Your First CLI Tool – A Beginner’s Guide 🗂 Category: PROGRAMMING 🕒 Date: 2025-05-13 | ⏱️ Read time: 8 min read From setup to your first command line application — step by step

📌 Survival Analysis When No One Dies: A Value-Based Approach 🗂 Category: STATISTICS 🕒 Date: 2025-05-13 | ⏱️ Read time: 11
📌 Survival Analysis When No One Dies: A Value-Based Approach 🗂 Category: STATISTICS 🕒 Date: 2025-05-13 | ⏱️ Read time: 11 min read A generalized version of Kaplan-Meier allows to model a continuous value (like money) instead of…

📌 Parquet File Format – Everything You Need to Know! 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-05-14 | ⏱️ Read time: 9 min
📌 Parquet File Format – Everything You Need to Know! 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-05-14 | ⏱️ Read time: 9 min read New data flavors require new ways for storing it! Learn everything you need to know…

📌 Efficient Graph Storage for Entity Resolution Using Clique-Based Compression 🗂 Category: DATA SCIENCE 🕒 Date: 2025-05-14
📌 Efficient Graph Storage for Entity Resolution Using Clique-Based Compression 🗂 Category: DATA SCIENCE 🕒 Date: 2025-05-14 | ⏱️ Read time: 7 min read Entity resolution systems face challenges with dense, interconnected graphs, and clique-based graph compression offers an…

📌 Strength in Numbers: Ensembling Models with Bagging and Boosting 🗂 Category: DATA SCIENCE 🕒 Date: 2025-05-15 | ⏱️ Read t
📌 Strength in Numbers: Ensembling Models with Bagging and Boosting 🗂 Category: DATA SCIENCE 🕒 Date: 2025-05-15 | ⏱️ Read time: 16 min read Mastering the fundamentals of bagging and boosting with simple examples

📌 The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated 🗂 Category: DATA ENGINEERING 🕒 Date:
📌 The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-05-15 | ⏱️ Read time: 8 min read A step closer to spatial AI with geospatial processing with Fabric

📌 Boost 2-Bit LLM Accuracy with EoRA 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-05-15 | ⏱️ Read time: 9 min read A tra
📌 Boost 2-Bit LLM Accuracy with EoRA 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-05-15 | ⏱️ Read time: 9 min read A training-free solution for extreme LLM compression.

📌 Explore the New World of Agent Protocols 🗂 Category: THE VARIABLE 🕒 Date: 2025-05-15 | ⏱️ Read time: 3 min read This wee
📌 Explore the New World of Agent Protocols 🗂 Category: THE VARIABLE 🕒 Date: 2025-05-15 | ⏱️ Read time: 3 min read This week, we focus on helping you gain a deeper understanding of MCP and other…

📌 How to Learn the Math Needed for Machine Learning 🗂 Category: MATH 🕒 Date: 2025-05-15 | ⏱️ Read time: 7 min read A break
📌 How to Learn the Math Needed for Machine Learning 🗂 Category: MATH 🕒 Date: 2025-05-15 | ⏱️ Read time: 7 min read A breakdown of the three fundamental math fields required for machine learning: statistics, linear algebra,…

📌 Understanding Random Forest using Python (scikit-learn) 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-05-15 | ⏱️ Read time:
📌 Understanding Random Forest using Python (scikit-learn) 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-05-15 | ⏱️ Read time: 9 min read A Random Forest is a powerful machine learning algorithm that can be used for classification…

📌 Google’s AlphaEvolve Is Evolving New Algorithms — And It Could Be a Game Changer 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 D
📌 Google’s AlphaEvolve Is Evolving New Algorithms — And It Could Be a Game Changer 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-15 | ⏱️ Read time: 6 min read A blend of LLMs’ creative generation capabilities with genetic algorithms