Machine Learning with Python

前往频道在 Telegram

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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网络:Machine Learning with Python 印度5 035 教育2 428...

📈 Telegram 频道 Machine Learning with Python 的分析概览

频道 Machine Learning with Python (@codeprogrammer) 英语语言赛道中的是活跃参与者。目前社区聚集了 67 833 名订阅者，在教育类别中位列第 2 428，并在印度地区排名第 5 035 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 67 833 名订阅者。

根据 15 六月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 82，过去 24 小时变化为 13，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 4.40%。内容发布后 24 小时内通常能获得 1.74% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 2 983 次浏览，首日通常累积 1 177 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 5。
主题关注点： 内容集中在 insidead, learning, degree, evaluation, algorithm 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

凭借高频更新（最新数据采集于 16 六月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为教育类别中的关键影响点。

67 833

订阅者

+1324 小时

+187 天

+8230 天

2 983

帖子浏览量

~ 1 17724 小时

~ 1 70948 小时

4.40%

参与率

~ 4

每日帖子数

Ads index

beta

帖子存档

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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
cd Savant/samples/peoplenet_detector
git lfs pull

▪Github: https://github.com/insight-platform/Savant

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