Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
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Сеть:📈 Аналитический обзор Telegram-канала Machine Learning with Python
Канал Machine Learning with Python (@codeprogrammer) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 68 109 подписчиков, занимая 2 375 место в категории Образование и 4 809 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 68 109 подписчиков.
Согласно последним данным от 26 августа, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 120, а за последние 24 часа — -14, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 4.55%. В первые 24 часа после публикации контент обычно набирает 2.02% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 3 099 просмотров. В течение первых суток публикация набирает 1 378 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 5.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как insidead, learning, degree, evaluation, algorithm.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Благодаря высокой частоте обновлений (последние данные получены 27 августа, 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 Образование.
68 109
Подписчики
-1424 часа
-667 дней
+12030 день
Архив постов
🧩 Local AI is no longer just a toy project.
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This post breaks down what is actually usable right now: Qwen, Gemma, Llama, Ollama, Chroma/LanceDB, local RAG, Jupyter AI, hardware limits, and where local video still hurts.
Read the local stack
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Repost from Machine Learning
Multi-Label Text Classification with Scikit-LLM 📝
In this article, you will learn how to perform multi-label text classification using large language models and the scikit-LLM library, without the need for labeled training data or complex model training. 🚀
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What multi-label classification is and why it matters for nuanced text analysis. 📊
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How to load a real-world dataset and run multi-label sentiment predictions using a familiar scikit-learn-style workflow. 📈
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