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 Python India5 036 Educación2 429...

📈 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 67 826 suscriptores, ocupando la posición 2 429 en la categoría Educación y el puesto 5 036 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 826 suscriptores.

Según los últimos datos del 14 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 66, y en las últimas 24 horas de 5, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.52%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.70% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 3 064 visualizaciones. En el primer día suele acumular 1 155 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 15 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.

67 826

Suscriptores

+524 horas

Sin datos7 días

+6630 días

3 064

Visitas de la publicación

~ 1 15524 horas

~ 1 68748 horas

4.52%

Tasa de compromiso

~ 4

Mensajes por día

Ads index

beta

Archivo de publicaciones

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Repost from Data Science Books

We have just launched a fundraising campaign for the channel to ensure continued quality service We upload the book via Internet data, and this is expensive for us Participate and contribute to the donation campaign until the target amount is reached Members who will contribute to the donation campaign will receive a free subscription to the paid channel and a LinkedIn grant Donate link: https://boosty.to/datascienceteam/donate

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Repost from Data Science Books

We have just launched a fundraising campaign for the channel to ensure continued quality service We upload the book via Internet data, and this is expensive for us Participate and contribute to the donation campaign until the target amount is reached Donate link: https://boosty.to/datascienceteam/donate

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غدا سنطلق حملة جمع تبرعات للقناة، وسيتم عرض نسبة التقدم أولا من خلال رسالة مثبتة نأمل منكم المشاركة في التبرعات إذا كنتم قادرين على التبرع Tomorrow we will launch a fundraising campaign for the channel, and the progress rate will be displayed first through a pinned message We hope that you will participate in donations if you are able to donate

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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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💼 Briefcase Briefcase is a tool for converting a Python project into a standalone native application. ▪ Github: https://github.com/beeware/briefcase ▪Tutorial: https://briefcase.readthedocs.io/en/stable/ https://t.me/CodeProgrammer Please more reaction with our posts

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