Machine Learning & Artificial Intelligence | Data Science Free Courses
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Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun
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Сеть:📈 Аналитический обзор Telegram-канала Machine Learning & Artificial Intelligence | Data Science Free Courses
Канал Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 68 655 подписчиков, занимая 2 395 место в категории Образование и 414 место в регионе Малайзия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 68 655 подписчиков.
Согласно последним данным от 23 октября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 226, а за последние 24 часа — -11, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 3.95%. В первые 24 часа после публикации контент обычно набирает 1.07% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 715 просмотров. В течение первых суток публикация набирает 735 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 4.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как sellerflash, waybienad, pricing, buybox, buyer.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfun”
Благодаря высокой частоте обновлений (последние данные получены 24 октября, 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.
68 655
Подписчики
-1124 часа
-77 дней
+22630 дней
Архив постов
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Normalization vs Standardization: Why They’re Not the Same
People treat these two as interchangeable. they’re not.
👉 Normalization (Min-Max scaling):
Compresses values to 0–1.
Useful when magnitude matters (pixel values, distances).
👉 Standardization (Z-score):
Centers data around mean=0, std=1.
Useful when distribution shape matters (linear/logistic regression, PCA).
🔑 Key idea:
Normalization preserves relative proportions.
Standardization preserves statistical structure.
Pick the wrong one, and your model’s geometry becomes distorted.
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𝐖𝐡𝐚𝐭 𝐆𝐨𝐨𝐠𝐥𝐞 𝐣𝐮𝐬𝐭 𝐮𝐧𝐥𝐨𝐜𝐤𝐞𝐝 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐰𝐨𝐫𝐥𝐝:
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𝐌𝐲 𝐭𝐚𝐤𝐞 𝐚𝐬 𝐚𝐧 𝐀𝐈 𝐥𝐞𝐚𝐝𝐞𝐫:
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What counted as “advanced knowledge” two years ago is basic literacy today.
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