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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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Machine Learning with PythonСеть:Machine Learning with PythonСирия225Технологии и приложения3 327

📈 Аналитический обзор Telegram-канала Machine Learning

Канал Machine Learning (@machinelearning9) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 40 373 подписчиков, занимая 3 327 место в категории Технологии и приложения и 225 место в регионе Сирия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 40 373 подписчиков.

Согласно последним данным от 12 июля, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 399, а за последние 24 часа — 24, при этом общий охват остаётся высоким.

  • Статус верификации: Не верифицирован
  • Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.42%. В первые 24 часа после публикации контент обычно набирает 1.74% реакций от общего числа подписчиков.
  • Охват публикаций: В среднем каждый пост получает 979 просмотров. В течение первых суток публикация набирает 703 просмотров.
  • Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 4.
  • 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 13 июля, 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.

40 373
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+2424 часа
+1257 дней
+39930 день
Архив постов
Machine Learning
Machine Learningverified-telegram-icon
40 393
📌 A Visual Guide to Tuning Random Forest Hyperparameters 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-04 | ⏱️ Read time: 8 min
📌 A Visual Guide to Tuning Random Forest Hyperparameters 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-04 | ⏱️ Read time: 8 min read How hyperparameter tuning visually changes random forests

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📌 Should We Use LLMs As If They Were Swiss Knives? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-04 | ⏱️ Read time:
📌 Should We Use LLMs As If They Were Swiss Knives? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-04 | ⏱️ Read time: 9 min read A logic game performance comparison between popular LLMs and a custom-made algorithm

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📌 Tool Masking: The Layer MCP Forgot 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-05 | ⏱️ Read time: 16 min read Tool masking fo
📌 Tool Masking: The Layer MCP Forgot 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-05 | ⏱️ Read time: 16 min read Tool masking for AI improves AI agents: shape MCP tool surfaces to cut tokens and…

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📌 Zero-Inflated Data: A Comparison of Regression Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-05 | ⏱️ Read time: 13 min
📌 Zero-Inflated Data: A Comparison of Regression Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-05 | ⏱️ Read time: 13 min read How to detect it and which model to choose.

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📌 AI Operations Under the Hood: Challenges and Best Practices 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-09-05 | ⏱️ Read ti
📌 AI Operations Under the Hood: Challenges and Best Practices 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-09-05 | ⏱️ Read time: 18 min read Building robust, reproducible, and reliable GenAI applications requires a framework of continuous improvement, rigorous evaluation,…

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📌 Showcasing Your Work on HuggingFace Spaces 🗂 Category: PRODUCTIVITY 🕒 Date: 2025-09-05 | ⏱️ Read time: 9 min read Buildi
📌 Showcasing Your Work on HuggingFace Spaces 🗂 Category: PRODUCTIVITY 🕒 Date: 2025-09-05 | ⏱️ Read time: 9 min read Building an app is exciting – but sharing it is where the real value kicks…

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📌 How to Context Engineer to Optimize Question Answering Pipelines 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-05 |
📌 How to Context Engineer to Optimize Question Answering Pipelines 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-05 | ⏱️ Read time: 9 min read Learn how to apply context engineering to enhance your question answering systems.

Machine Learning
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40 393
📌 Tool Masking: The Layer MCP Forgot 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-05T07:00:00-05:00 | ⏱ Read time: 16 min read T
📌 Tool Masking: The Layer MCP Forgot 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-05T07:00:00-05:00 | ⏱ Read time: 16 min read Tool masking for AI improves AI agents: shape MCP tool surfaces to cut tokens and…

Machine Learning
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40 393
📌 Zero-Inflated Data: A Comparison of Regression Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-05T08:30:00-05:00 | ⏱ Rea
📌 Zero-Inflated Data: A Comparison of Regression Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-05T08:30:00-05:00 | ⏱ Read time: 13 min read How to detect it and which model to choose.

Machine Learning
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40 393
📌 AI Operations Under the Hood: Challenges and Best Practices 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-09-05T10:00:00-05:
📌 AI Operations Under the Hood: Challenges and Best Practices 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-09-05T10:00:00-05:00 | ⏱ Read time: 18 min read Building robust, reproducible, and reliable GenAI applications requires a framework of continuous improvement, rigorous evaluation,…

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40 393
📌 Showcasing Your Work on HuggingFace Spaces 🗂 Category: PRODUCTIVITY 🕒 Date: 2025-09-05T11:30:00-05:00 | ⏱ Read time: 9 m
📌 Showcasing Your Work on HuggingFace Spaces 🗂 Category: PRODUCTIVITY 🕒 Date: 2025-09-05T11:30:00-05:00 | ⏱ Read time: 9 min read Building an app is exciting – but sharing it is where the real value kicks…

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📌 How to Context Engineer to Optimize Question Answering Pipelines 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-05T12
📌 How to Context Engineer to Optimize Question Answering Pipelines 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-05T12:30:00-05:00 | ⏱ Read time: 9 min read Learn how to apply context engineering to enhance your question answering systems.

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Repost from Github Top Repositories
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#data_science #machine_learning #data_mining #deep_learning #genetic_algorithm #deep_reinforcement_learning #machine_learning_from_scratch
================================== 🧠 By: https://t.me/DataScienceM

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No one believed I could double my portfolio in just 30 days… but then I discovered the Prop Mastery tricks top traders keep s
No one believed I could double my portfolio in just 30 days… but then I discovered the Prop Mastery tricks top traders keep secret. Want to know how? The real strategies are hidden right here — and only a few catch on before it’s too late! #إعلان InsideAds

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“I was laughed at when I bought crypto in 2019. Now my portfolio’s up 1200% — and friends keep asking for ‘the secret’… But n
“I was laughed at when I bought crypto in 2019. Now my portfolio’s up 1200% — and friends keep asking for ‘the secret’… But nobody talks about the brutal truths I learned along the way. Want to see what everyone’s missing? 👉 See it here #إعلان InsideAds

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Think you know your football history? Who scored a hat-trick against Real Madrid in yellow and black? Every day, Footy Riddle
Think you know your football history? Who scored a hat-trick against Real Madrid in yellow and black? Every day, Footy Riddles ⚽ drops clever puzzles about iconic players, unforgettable moments, and legendary matches. Challenge your football brain and see if you can guess before anyone else! Only the quickest fans know all the answers—are you one of them? #إعلان InsideAds

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90% трейдеров сливают из-за ручного анализа и эмоций? TRUE — первый AI-протокол на Solana, который сам учится на on-chain дан
90% трейдеров сливают из-за ручного анализа и эмоций? TRUE — первый AI-протокол на Solana, который сам учится на on-chain данных, выдает персональные стратегии и делает трейдинг понятным на естественном языке. Именно сейчас можно получить доступ к запуску и участвовать в токенсейле $TRUE, пока условия максимально выгодные. Трейдинг будущего начинается здесь — не упусти шанс быть первым! Присоединяйся #إعلان InsideAds

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What if you could skip all the chart noise and just ask AI to invest for you? 90% of traders lose money… but the game changes
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🔥 Trending Repository: abogen 📝 Description: Generate audiobooks from EPUBs, PDFs and text with synchronized captions. 🔗 R
🔥 Trending Repository: abogen 📝 Description: Generate audiobooks from EPUBs, PDFs and text with synchronized captions. 🔗 Repository URL: https://github.com/denizsafak/abogen 🌐 Website: https://pypi.org/project/abogen/ 📖 Readme: https://github.com/denizsafak/abogen#readme 📊 Statistics: 🌟 Stars: 3.1K stars 👀 Watchers: 18 🍴 Forks: 159 forks 💻 Programming Languages: Python - Batchfile - Dockerfile 🏷️ Related Topics:
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🔥 Trending Repository: cognitive-load 📝 Description: 🧠 Cognitive Load is what matters 🔗 Repository URL: https://github.co
🔥 Trending Repository: cognitive-load 📝 Description: 🧠 Cognitive Load is what matters 🔗 Repository URL: https://github.com/zakirullin/cognitive-load 📖 Readme: https://github.com/zakirullin/cognitive-load#readme 📊 Statistics: 🌟 Stars: 9.4K stars 👀 Watchers: 86 🍴 Forks: 200 forks 💻 Programming Languages: Not available 🏷️ Related Topics: Not available ================================== 🧠 By: https://t.me/DataScienceM