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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Сітка:📈 Аналітичний огляд Telegram-каналу Machine Learning & Artificial Intelligence | Data Science Free Courses
Канал Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 68 655 підписників, посідаючи 2 395 місце в категорії Освіта та 414 місце у регіоні Малайзія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 68 655 підписників.
За останніми даними від 23 жовтня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 226, а за останні 24 години на -11, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 3.95%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.07% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 2 715 переглядів. Протягом першої доби публікація в середньому набирає 735 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 4.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як sellerflash, waybienad, pricing, buybox, buyer.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfun”
Завдяки високій частоті оновлень (останні дані отримано 24 жовтня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.
68 655
Підписники
-1124 години
-77 днів
+22630 днів
Архів дописів
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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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