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Machine Learning with Python

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

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📈 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 67 833 مشتركاً، محتلاً المرتبة 2 428 في فئة التعليم والمرتبة 5 035 في منطقة الهند.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 67 833 مشتركاً.

بحسب آخر البيانات بتاريخ 15 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 82، وفي آخر 24 ساعة بمقدار 13، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 4.40‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.74‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 2 983 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 177 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 5.
  • 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 16 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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😲 Awesome useful Python scripts Useful ready-made Python scripts. 1. JSON ↔️ CSV (Fig.1) 2. Password generator (Fig.2) 3. St
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✋ Hand gesture recognition import cv2 import mediapipe as mp # Initialize MediaPipe Hands module mp_hands = mp.solutions.hands hands = mp_hands.Hands() # Initialize MediaPipe Drawing module for drawing landmarks mp_drawing = mp.solutions.drawing_utils # Open a video capture object (0 for the default camera) cap = cv2.VideoCapture(0) while cap.isOpened(): ret, frame = cap.read() if not ret: continue # Convert the frame to RGB format frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # Process the frame to detect hands results = hands.process(frame_rgb) # Check if hands are detected if results.multi_hand_landmarks: for hand_landmarks in results.multi_hand_landmarks: # Draw landmarks on the frame mp_drawing.draw_landmarks(frame, hand_landmarks, mp_hands.HAND_CONNECTIONS) # Display the frame with hand landmarks cv2.imshow('Hand Recognition', frame) # Exit when 'q' is pressed if cv2.waitKey(1) & 0xFF == ord('q'): break # Release the video capture object and close the OpenCV windows cap.release() cv2.destroyAllWindows()

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Machine Learning with Python - إحصائيات وتحليلات قناة تيليجرام @codeprogrammer