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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Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 67 833 obunachidan iborat bo'lib, Taʼlim toifasida 2 428-o'rinni va Hindiston mintaqasida 5 035-o'rinni egallagan.

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15 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 82 ga, so‘nggi 24 soatda esa 13 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

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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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😲 Awesome useful Python scripts Useful ready-made Python scripts. 1. JSON ↔️ CSV (Fig.1) 2. Password generator (Fig.2) 3. String search from several files (Fig.3) 4. Retrieving all links from a given web page (Fig.4) 5. Add a watermark (Fig.5) 6. Parser and image loader from WEB page (Fig.6) 7. Sorting the download folder (Fig.7) 8. Bulk email sending from a CSV file (Fig.8) 9. Obtaining the IP address and hostname of the website (Fig.9) 10. Progress bar of the terminal (Fig.10) 🔗 Scripts: https://uproger.com/udivitelnye-sczenarii-python/ https://t.me/CodeProgrammer Please more reaction with our posts

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📈 Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide The process of building a stock price prediction model using Python. 1. Import required modules 2. Obtaining historical data on stock prices 3. Selection of features. 4. Definition of features and target variable 5. Preparing data for training 6. Separation of data into training and test sets 7. Building and training the model 8. Making forecasts 9. Trading Strategy Testing https://t.me/CodeProgrammer Please more reaction with our posts

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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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👁Savant: Supercharged Computer Vision and Video Analytics Framework on DeepStream

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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