متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
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
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 67 823 مشتركاً، محتلاً المرتبة 2 419 في فئة التعليم والمرتبة 5 033 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 67 823 مشتركاً.
بحسب آخر البيانات بتاريخ 11 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 56، وفي آخر 24 ساعة بمقدار 0، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 3.96%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 2.43% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 2 683 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 650 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 12 يونيو, 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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Coursera has launched a collaboration with the PAWS platform to enable students to self-fund using the Paws platform.
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Bayes’ theorem, clearly explained
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Amazing graphical representation of a neural net, never seen anything like it.
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NumPy cheat sheet for Data Scientists
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Python Pandas Cheat Sheet
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Using Normalization Layers to Improve Deep Learning Models
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How to deal with outliers clearly explained
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