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 68 671 подписчиков, занимая 2 398 место в категории Образование и 419 место в регионе Малайзия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 68 671 подписчиков.
Согласно последним данным от 21 октября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 274, а за последние 24 часа — 4, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 5.32%. В первые 24 часа после публикации контент обычно набирает 1.03% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 0 просмотров. В течение первых суток публикация набирает 705 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 0.
- 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 22 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.
68 671
Подписчики
+424 часа
+147 дней
+27430 дней
Архив постов
✅ Data Science Mistakes Beginners Should Avoid ⚠️📉
1️⃣ Skipping the Basics
• Jumping into ML without Python, Stats, or Pandas
✅ Build strong foundations in math, programming & EDA first
2️⃣ Not Understanding the Problem
• Applying models blindly
• Irrelevant features and metrics
✅ Always clarify business goals before coding
3️⃣ Treating Data Cleaning as Optional
• Training on dirty/incomplete data
✅ Spend time on preprocessing — it’s 70% of real work
4️⃣ Using Complex Models Too Early
• Overfitting small datasets
• Ignoring simpler, interpretable models
✅ Start with baseline models (Logistic Regression, Decision Trees)
5️⃣ No Evaluation Strategy
• Relying only on accuracy
✅ Use proper metrics (F1, AUC, MAE) based on problem type
6️⃣ Not Visualizing Data
• Missed outliers and patterns
✅ Use Seaborn, Matplotlib, Plotly for EDA
7️⃣ Poor Feature Engineering
• Feeding raw data into models
✅ Create meaningful features that boost performance
8️⃣ Ignoring Domain Knowledge
• Features don’t align with real-world logic
✅ Talk to stakeholders or do research before modeling
9️⃣ No Practice with Real Datasets
• Kaggle-only learning
✅ Work with messy, real-world data (open data portals, APIs)
🔟 Not Documenting or Sharing Work
• No GitHub, no portfolio
✅ Document notebooks, write blogs, push projects online
💬 Tap ❤️ for more!
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