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

Сітка:Machine Learning with Python Індія5 036 Освіта2 429...

📈 Аналітичний огляд Telegram-каналу Machine Learning with Python

Канал Machine Learning with Python (@codeprogrammer) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 67 826 підписників, посідаючи 2 429 місце в категорії Освіта та 5 036 місце у регіоні Індія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 67 826 підписників.

За останніми даними від 14 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 66, а за останні 24 години на 5, загальне охоплення залишається високим.

Статус верифікації: Не верифікований
Рівень залученості (ER): Середній показник залученості аудиторії становить 4.52%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.70% реакцій від загальної кількості підписників.
Охоплення публікацій: В середньому кожен допис отримує 3 064 переглядів. Протягом першої доби публікація в середньому набирає 1 155 переглядів.
Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 5.
Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як insidead, learning, degree, evaluation, algorithm.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Завдяки високій частоті оновлень (останні дані отримано 15 червня, 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.

67 826

Підписники

+524 години

Немає даних7 днів

+6630 день

3 064

Перегляди допису

~ 1 15524 години

~ 1 68748 годин

4.52%

Коефіцієнт залучення

~ 4

Дописів на день

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Архів дописів

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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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✋ Hand gesture recognition

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git clone https://github.com/insight-platform/Savant.git
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