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

Network:Machine Learning with Python India5 036 Education2 429...

📈 Analytical overview of Telegram channel Machine Learning with Python

Channel Machine Learning with Python (@codeprogrammer) in the English language segment is an active participant. Currently, the community unites 67 826 subscribers, ranking 2 429 in the Education category and 5 036 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 826 subscribers.

According to the latest data from 14 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 66 over the last 30 days and by 5 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 4.52%. Within the first 24 hours after publication, content typically collects 1.70% reactions from the total number of subscribers.
Post reach: On average, each post receives 3 064 views. Within the first day, a publication typically gains 1 155 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 15 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

67 826

Subscribers

+524 hours

No data7 days

+6630 days

3 064

Post views

~ 1 15524 hours

~ 1 68748 hours

4.52%

Engagement rate

~ 4

Posts per day

Ads index

beta

Posts Archive

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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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The largest library for downloading data science books and programming languages https://t.me/+MhmkscCzIYQ2MmM8

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https://t.me/code_stars 5 mins

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How to Download Files From URLs With Python https://realpython.com/python-download-file-from-url https://t.me/CodeProgrammer Please more reaction with our posts

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Code Stars Get ahead of the game with Code Stars! Our platform sends you notifications about the hottest GitHub repositories that are rapidly gaining popularity. Don't miss out - join us now and be the first to discover the trending repositories that everyone will be talking about soon!

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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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Data Structures Short Notes ✅

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🔥🖥Roadmap of free courses for learning Python and Machine learning. Roadmap бесплатных курсов для изучения Python и Machine learning. ▪Data Science ▪ AI/ML ▪ Web Dev 📌Курсы с сертификатами. 1. Start with this https://kaggle.com/learn/python 2. Take any one of these ❯ https://openclassrooms.com/courses/6900856-learn-programming-with-python ❯ https://scaler.com/topics/course/python-for-beginners/ ❯ https://simplilearn.com/learn-python-basics-free-course-skillup 3. Then take this https://netacad.com/courses/programming/pcap-programming-essentials-python 4. Attempt for this certification https://freecodecamp.org/learn/scientific-computing-with-python/ 5. Take it to next level ❯ Data Scrapping, NumPy, Pandas https://scaler.com/topics/course/python-for-data-science/ ❯ Data Analysis https://openclassrooms.com/courses/2304731-learn-python-basics-for-data-analysis ❯ Data Visualization https://kaggle.com/learn/data-visualization ❯ Django https://openclassrooms.com/courses/6967196-create-a-web-application-with-django ❯ Machine Learning http://developers.google.com/machine-learning/crash-course ❯ Deep Learning (TensorFlow) http://kaggle.com/learn/intro-to-deep-learning @pythonl

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🖥 Convert PDF to docx using Python ▪Github: https://github.com/dothinking/pdf2docx https://t.me/CodeProgrammer

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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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