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 67 804 suscriptores, ocupando la posición 2 419 en la categoría Educación y el puesto 4 901 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 804 suscriptores.
Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 107, y en las últimas 24 horas de 23, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.73%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.30% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 210 visualizaciones. En el primer día suele acumular 884 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 4.
- 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 24 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.
Carga de datos en curso...
| Fecha | Crecimiento de Suscriptores | Menciones | Canales | |
| 24 junio | +3 | |||
| 23 junio | +35 | |||
| 22 junio | +15 | |||
| 21 junio | +3 | |||
| 20 junio | +14 | |||
| 19 junio | +6 | |||
| 18 junio | +12 | |||
| 17 junio | +12 | |||
| 16 junio | +19 | |||
| 15 junio | +26 | |||
| 14 junio | +21 | |||
| 13 junio | +17 | |||
| 12 junio | +23 | |||
| 11 junio | +4 | |||
| 10 junio | +13 | |||
| 09 junio | +14 | |||
| 08 junio | +11 | |||
| 07 junio | +18 | |||
| 06 junio | +12 | |||
| 05 junio | +22 | |||
| 04 junio | +30 | |||
| 03 junio | +17 | |||
| 02 junio | +35 | |||
| 01 junio | +25 |
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| 4 | My favorite way to work with multiple filters in pandas.Series — not a chain of .loc, but a single mask. 🐼
The chain looks neat, but breaks on real data and easily gives unexpected results:
s = pd.Series([10, 15, 20, 25, 30])
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mask = (s > 20) & (s % 2 == 1)
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