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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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📈 Аналитический обзор Telegram-канала Machine Learning

Канал Machine Learning (@machinelearning9) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 40 072 подписчиков, занимая 3 398 место в категории Технологии и приложения и 232 место в регионе Сирия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 40 072 подписчиков.

Согласно последним данным от 23 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 379, а за последние 24 часа — 30, при этом общий охват остаётся высоким.

  • Статус верификации: Не верифицирован
  • Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 1.92%. В первые 24 часа после публикации контент обычно набирает 1.16% реакций от общего числа подписчиков.
  • Охват публикаций: В среднем каждый пост получает 770 просмотров. В течение первых суток публикация набирает 466 просмотров.
  • Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
  • Тематические интересы: Контент сосредоточен на ключевых темах, таких как distance, insidead, gpu, learning, degree.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Благодаря высокой частоте обновлений (последние данные получены 24 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.

40 072
Подписчики
+3024 часа
+337 дней
+37930 день
Архив постов
📌 An End-to-End Guide to Beautifying Your Open-Source Repo with Agentic AI 🗂 Category: LLM APPLICATIONS 🕒 Date: 2026-02-20
📌 An End-to-End Guide to Beautifying Your Open-Source Repo with Agentic AI 🗂 Category: LLM APPLICATIONS 🕒 Date: 2026-02-20 | ⏱️ Read time: 17 min read The guide to automated improvement of scientific and industrial repositories using open-source AI agents #DataScience #AI #Python

📌 Donkeys, Not Unicorns 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-20 | ⏱️ Read time: 8 min read The New Rules of
📌 Donkeys, Not Unicorns 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-20 | ⏱️ Read time: 8 min read The New Rules of Entrepreneurship in the Era of Commoditized Magic #DataScience #AI #Python

📌 AI in Multiple GPUs: How GPUs Communicate 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-19 | ⏱️ Read time: 5 min r
📌 AI in Multiple GPUs: How GPUs Communicate 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-19 | ⏱️ Read time: 5 min read A deep dive into the hardware infrastructure that enables multi-GPU communication for AI workloads #DataScience #AI #Python

📌 AlpamayoR1: Large Causal Reasoning Models for Autonomous Driving 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-19
📌 AlpamayoR1: Large Causal Reasoning Models for Autonomous Driving 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-19 | ⏱️ Read time: 9 min read All you need to know about Chain of Causation reasoning and the current state of… #DataScience #AI #Python

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📌 Understanding the Chi-Square Test Beyond the Formula 🗂 Category: STATISTICS 🕒 Date: 2026-02-19 | ⏱️ Read time: 17 min re
📌 Understanding the Chi-Square Test Beyond the Formula 🗂 Category: STATISTICS 🕒 Date: 2026-02-19 | ⏱️ Read time: 17 min read How categorical data becomes statistical evidence. #DataScience #AI #Python

📌 The Missing Curriculum: Essential Concepts For Data Scientists in the Age of AI Coding Agents 🗂 Category: PROGRAMMING 🕒
📌 The Missing Curriculum: Essential Concepts For Data Scientists in the Age of AI Coding Agents 🗂 Category: PROGRAMMING 🕒 Date: 2026-02-19 | ⏱️ Read time: 13 min read AI can write the code, but you have to steer the ship. Master the knowledge… #DataScience #AI #Python

📌 Agentic AI for Modern Deep Learning Experimentation 🗂 Category: AGENTIC AI 🕒 Date: 2026-02-18 | ⏱️ Read time: 14 min rea
📌 Agentic AI for Modern Deep Learning Experimentation 🗂 Category: AGENTIC AI 🕒 Date: 2026-02-18 | ⏱️ Read time: 14 min read Stop babysitting training runs. Start shipping research. Autonomous experiment management built for/by deep learning engineers. #DataScience #AI #Python

📌 Why Every Analytics Engineer Needs to Understand Data Architecture 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-02-18 | ⏱️
📌 Why Every Analytics Engineer Needs to Understand Data Architecture 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-02-18 | ⏱️ Read time: 11 min read Get the data architecture right, and everything else becomes easier. I know it sounds simple,… #DataScience #AI #Python

📌 Building Cost-Efficient Agentic RAG on Long-Text Documents in SQL Tables 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-
📌 Building Cost-Efficient Agentic RAG on Long-Text Documents in SQL Tables 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-02-18 | ⏱️ Read time: 13 min read Designing a hybrid SQL + vector retrieval system without schema changes, data migration, or performance… #DataScience #AI #Python

📌 Can AI Solve Failures in Your Supply Chain? 🗂 Category: AGENTIC AI 🕒 Date: 2026-02-18 | ⏱️ Read time: 16 min read When y
📌 Can AI Solve Failures in Your Supply Chain? 🗂 Category: AGENTIC AI 🕒 Date: 2026-02-18 | ⏱️ Read time: 16 min read When your warehouse and transportation teams blame each other for late deliveries, who’s right? We… #DataScience #AI #Python

📌 Use OpenClaw to Make a Personal AI Assistant 🗂 Category: AGENTIC AI 🕒 Date: 2026-02-17 | ⏱️ Read time: 10 min read Learn
📌 Use OpenClaw to Make a Personal AI Assistant 🗂 Category: AGENTIC AI 🕒 Date: 2026-02-17 | ⏱️ Read time: 10 min read Learn how to set up OpenClaw as a personalized AI agent #DataScience #AI #Python

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📌 Advance Planning for AI Project Evaluation 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-17 | ⏱️ Read time: 7 min
📌 Advance Planning for AI Project Evaluation 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-17 | ⏱️ Read time: 7 min read The work to do before the work begins #DataScience #AI #Python

📌 Iron Triangles: Powerful Tools for Analyzing Trade-Offs in AI Product Development 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒
📌 Iron Triangles: Powerful Tools for Analyzing Trade-Offs in AI Product Development 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-17 | ⏱️ Read time: 9 min read Conceptual overview and practical guidance #DataScience #AI #Python

🔖 An excellent resource for learning about neural networks We're sharing a cool resource for learning about neural networks, offering clear, step-by-step instruction with dynamic visualizations and easy-to-understand explanations. In addition, you'll find many other useful materials on machine learning on the site. Find and use it — https://mlu-explain.github.io/neural-networks/ tags: #AI #ML #PYTHON ➡ @CODEPROGRAMMER

📌 Building a LangGraph Agent from Scratch 🗂 Category: AGENTIC AI 🕒 Date: 2026-02-17 | ⏱️ Read time: 10 min read Everything
📌 Building a LangGraph Agent from Scratch 🗂 Category: AGENTIC AI 🕒 Date: 2026-02-17 | ⏱️ Read time: 10 min read Everything you need to know to get started #DataScience #AI #Python

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Transformers in Action 2026
Transformers in Action 2026

📌 The Strangest Bottleneck in Modern LLMs 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-16 | ⏱️ Read time: 11 min re
📌 The Strangest Bottleneck in Modern LLMs 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-16 | ⏱️ Read time: 11 min read Why insanely fast GPUs still can’t make LLMs feel instant #DataScience #AI #Python