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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

Red: Machine Learning with PythonIndia4 586Educación2 406...

📈 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 68 482 suscriptores, ocupando la posición 2 406 en la categoría Educación y el puesto 4 586 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 482 suscriptores.

Según los últimos datos del 04 noviembre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -75, y en las últimas 24 horas de 7, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.38%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.57% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 2 314 visualizaciones. En el primer día suele acumular 1 073 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 9.
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 05 noviembre, 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.

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68 482

Suscriptores

+724 horas

-597 días

-7530 días

2 314

Visitas de la publicación

~ 1 07324 horas

~ 1 59648 horas

3.38%

Tasa de compromiso

~ 2

Mensajes por día

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🖥 Get domain name information using Python $pip install whois $pip install whois

>>> import whois
>>> domain = whois.query('google.com')

>>> print(domain.dict)
{
 'expiration_date': datetime.datetime(2020, 9, 14, 0, 0),
 'last_updated': datetime.datetime(2011, 7, 20, 0, 0),
 'registrar': 'MARKMONITOR INC.',
 'name': 'google.com',
 'creation_date': datetime.datetime(1997, 9, 15, 0, 0)
}

>>> print(domain.name)
google.com

>>> print(domain.expiration_date)
2024-09-14 00:00:00

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How to Perform Face Detection with Deep Learning Face detection is a computer vision problem that involves finding faces in photos. It is a trivial problem for humans to solve and has been solved reasonably well by classical feature-based techniques, such as the cascade classifier. More recently deep learning methods have achieved state-of-the-art results on standard benchmark face detection datasets. One example is the Multi-task Cascade Convolutional Neural Network, or MTCNN for short. In this tutorial, you will discover how to perform face detection in Python using classical and deep learning models. https://machinelearningmastery.com/how-to-perform-face-detection-with-classical-and-deep-learning-methods-in-python-with-keras/

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