Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
Mostrar más📈 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 67 902 suscriptores, ocupando la posición 2 401 en la categoría Educación y el puesto 4 810 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 902 suscriptores.
Según los últimos datos del 13 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 89, y en las últimas 24 horas de -8, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.73%. 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 1 175 visualizaciones. En el primer día suele acumular 1 063 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
- 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 14 julio, 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.
Carga de datos en curso...
| Fecha | Crecimiento de Suscriptores | Menciones | Canales | |
| 13 julio | +5 | |||
| 12 julio | +28 | |||
| 11 julio | +12 | |||
| 10 julio | +12 | |||
| 09 julio | +22 | |||
| 08 julio | +22 | |||
| 07 julio | +14 | |||
| 06 julio | +24 | |||
| 05 julio | +19 | |||
| 04 julio | +17 | |||
| 03 julio | +17 | |||
| 02 julio | +20 | |||
| 01 julio | 0 |
| 2 | Diving deep into Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP. 🤖🧠
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| 7 | Reinforcement Learning Methods and Tutorials
In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.
Learning Resources: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow
Here's a collection of simple materials on methods and practical guides, covering both basic reinforcement learning algorithms and modern, recently developed, and updated advanced algorithms. | 2 545 |
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It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step:
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