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Machine Learning with Python

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 68 102 suscriptores, ocupando la posición 2 372 en la categoría Educación y el puesto 4 808 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 102 suscriptores.

Según los últimos datos del 27 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 112, 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 4.52%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.90% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 3 077 visualizaciones. En el primer día suele acumular 1 291 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 28 agosto, 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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Stop using if obj == None, use if obj is None In Python, when you write: obj == None you're not directly checking if obj is t
Stop using if obj == None, use if obj is None In Python, when you write:
obj == None
you're not directly checking if obj is the value None. Instead, you're asking if the object is equal to None. Yes, in many cases, the result will be the same as for the code:
obj is None
But the behavior of these two variants is different, and this difference is important. When you use:
obj == None
Python calls the __eq__ method on the object. That is, the object itself decides what it means to be "equal to None". And this method can be overridden. If obj is an instance of a class in which __eq__ is implemented so that when compared with None, it returns True (even if the object is not actually None), then obj == None may mistakenly give True. Example:
class Weird:
    def __eq__(self, other):
        return True  # Always asserts that it's equal

obj = Weird()

print(obj == None)  # True
print(obj is None)  # False
Here, it can be seen that obj == None returns True due to the custom behaeqf the __eq__ operator in the class. Therefore, when using obj == None, the result is not always predictable. On the other hand, when you write:
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