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لا تقع ضحية للمخادعين! تيليمتريو يكتشف ويُميّز هذه القنوات 👉 إذا كنت تريد رؤية العلامة، اشترك

Machine Learning with Python

Machine Learning with Python

الذهاب إلى القناة على Telegram

Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

إظهار المزيد

📈 نظرة تحليلية على قناة تيليجرام Machine Learning with Python

تُعد قناة Machine Learning with Python (@codeprogrammer) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 68 559 مشتركاً، محتلاً المرتبة 2 405 في فئة التعليم والمرتبة 4 691 في منطقة الهند.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 68 559 مشتركاً.

بحسب آخر البيانات بتاريخ 26 أكتوبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 211، وفي آخر 24 ساعة بمقدار -16، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 3.52‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.51‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 2 414 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 035 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 6.
  • الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل insidead, learning, degree, evaluation, algorithm.

📝 الوصف وسياسة المحتوى

يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 27 أكتوبر, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.

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Thanks 🙏

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Curated list of distributed systems papers and books 📚📖 https://github.com/theanalyst/awesome-distributed-systems ✨ Join Be
Curated list of distributed systems papers and books 📚📖 https://github.com/theanalyst/awesome-distributed-systems ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more. ✅ 13 courses live + 40+ coming soon 🎯 One access, lifetime updates 🔑 Use code: PRESALE-BOOK-WAVE-2GFG 👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

Guide to scalable system design and interviews 🚀📊 https://github.com/karanpratapsingh/system-design #SystemDesign #Scalabil
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My favorite way to work with multiple filters in pandas.Series — not a chain of .loc, but a single mask. 🐼 The chain looks neat, but breaks on real data and easily gives unexpected results:
s = pd.Series([10, 15, 20, 25, 30])
s.loc[s > 20].loc[s % 2 == 1]
The problem is that the second .loc again looks at the original s, not the already filtered result. The logic gets messy. 🤯 It's more reliable to gather everything into one expression:
s = pd.Series([10, 15, 20, 25, 30])

mask = (s > 20) & (s % 2 == 1)
result = s.loc[mask]
One mask, one point of truth. ✅ It's easier to debug. Fewer surprises when the code grows. 🚀 #Pandas #Python #DataScience #CodingTips #DataEngineering #Debugging ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more. ✅ 13 courses live + 40+ coming soon 🎯 One access, lifetime updates 🔑 Use code: PRESALE-BOOK-WAVE-2GFG 👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

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