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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📈 Análisis del canal de Telegram Machine Learning with Python

El canal Machine Learning with Python (@codeprogrammer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 67 833 suscriptores, ocupando la posición 2 428 en la categoría Educación y el puesto 5 035 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 833 suscriptores.

Según los últimos datos del 15 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 82, y en las últimas 24 horas de 13, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.40%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.74% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 2 983 visualizaciones. En el primer día suele acumular 1 177 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
Intereses temáticos: El contenido se centra en temas clave como insidead, learning, degree, evaluation, algorithm.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 16 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.

67 833

Suscriptores

+1324 horas

+187 días

+8230 días

2 983

Visitas de la publicación

~ 1 17724 horas

~ 1 70948 horas

4.40%

Tasa de compromiso

~ 4

Mensajes por día

Ads index

beta

Archivo de publicaciones

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👱‍♂️ Creating Face Swaps with Python and OpenCV Step 1: Face Detection

import cv2
def detect_face(image_path):
    # Load the face detection classifier
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

    # Read and convert the image to grayscale
    image = cv2.imread(image_path)
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Detect faces in the image
    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5)

    # Assuming there's only one face in the image, return its coordinates
    if len(faces) == 1:
        return faces[0]
    else:
        return None

Step 2: Swapping Faces

def main():
    # Paths to the input images
    image_path_1 = 'path_to_image1.jpg'
    image_path_2 = 'path_to_image2.jpg'

    # Detect the face in the second image
    face_coords_2 = detect_face(image_path_2)
    if face_coords_2 is None:
        print("No face found in the second image.")
        return

    # Load and resize the source face
    image_1 = cv2.imread(image_path_1)
    face_width, face_height = face_coords_2[2], face_coords_2[3]
    image_1_resized = cv2.resize(image_1, (face_width, face_height))

    # Extract the target face region from the second image
    image_2 = cv2.imread(image_path_2)
    roi = image_2[face_coords_2[1]:face_coords_2[1] + face_height, face_coords_2[0]:face_coords_2[0] + face_width]

    # Flip the target face horizontally
    reflected_roi = cv2.flip(roi, 1)

    # Blend the two faces together
    alpha = 0.7
    blended_image = cv2.addWeighted(image_1_resized, alpha, reflected_roi, 1 - alpha, 0)

    # Replace the target face region with the blended image
    image_2[face_coords_2[1]:face_coords_2[1] + face_height, face_coords_2[0]:face_coords_2[0] + face_width] = blended_image

    # Display the result
    cv2.imshow('Blended Image', image_2)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

if name == "main":
    main()

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67 831

👱‍♂️ Creating Face Swaps with Python and OpenCV https://t.me/CodeProgrammer More reaction please ⭐️💐⭐️

67 831

How to Train an Object Detection Model with Keras https://machinelearningmastery.com/how-to-train-an-object-detection-model-with-keras/ https://t.me/CodeProgrammer More reaction please ⭐️💐⭐️

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Daily python books https://t.me/DataScience4 Daily python books https://t.me/DataScience4

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⚡ Top 100+ Machine Learning Projects for 2023 [with Source Code] In this article, you will find 100+ of the best machine learning projects and ideas that will be useful for both beginners and experienced professionals. 📌Projects: https://www.geeksforgeeks.org/machine-learning-projects/ https://t.me/CodeProgrammer More reaction please ⭐️💐⭐️

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📚 9 must-have Python developer tools. 1. PyCharm IDE 2. Jupyter notebook 3. Keras 4. Pip Package 5. Python Anywhere 6. Scikit-Learn 7. Sphinx 8. Selenium 9. Sublime Text https://t.me/CodeProgrammer More reaction please ⭐️💐⭐️

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👨‍🎓Harvard CS50’s Artificial Intelligence with Python – Full University Course This free course from Harvard University explores the concepts and algorithms behind modern artificial intelligence. 🎞 Video: https://www.youtube.com/watch?v=5NgNicANyqM 📌 Course resources: https://cs50.harvard.edu/ai/2020/ https://t.me/CodeProgrammer More reaction please 👌

67 831

🖥 Importing Data from SQL Server to Excel with Multiple Sheets using Python 📝 Source Code: https://github.com/danis111/Importing-Data-from-SQL-Server-to-Excel-with-Multiple-Sheets-using-Python/tree/main https://t.me/CodeProgrammer More reaction please 👌

67 831

🖐 Python Mouse Control Remotely With Your Hand. ▪ Source Code: https://gist.github.com/Develp10/3d605ce6ef017fdfc3e66e147ec9cc18 https://t.me/CodeProgrammer

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https://t.me/DataScience4 Only 11 members

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This channels is for Programmers, Coders, Software Engineers. 0- Python 1- Data Science 2- Machine Learning 3- Data Visualization 4- Artificial Intelligence 5- Data Analysis 6- Statistics 7- Deep Learning 8- programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/DataScienceM

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35 Best+FREE Coursera Courses for Data Science and Machine Learning! https://www.mltut.com/best-coursera-courses-for-data-science/

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🖥 8 delightful Python scripts that will brighten your day 8 cool python scripts to brighten up your day . These little gems will add some fun to your programming projects. 1. Speed test 2. Convert photo to cartoon format 3. Site status output 4. Image enhancement 5. Creating a web bot 6. Conversion: Hex to RGB 7. Convert PDF to images 8. Get song lyrics https://t.me/CodeProgrammer

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