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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 67 821 suscriptores, ocupando la posición 2 404 en la categoría Educación y el puesto 5 049 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 821 suscriptores.

Según los últimos datos del 05 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 77, y en las últimas 24 horas de 9, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.60%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.50% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 1 767 visualizaciones. En el primer día suele acumular 1 695 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 6.
  • 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 07 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.

67 821
Suscriptores
+924 horas
+587 días
+7730 días
Archivo de publicaciones
Have you tried running the code? yes 👍 | No 👎

Create a Wi-Fi QR code in Python in a couple of seconds pip install wifi_qrcode_generator import wifi_qrcode_generator.genera
Create a Wi-Fi QR code in Python in a couple of seconds
pip install wifi_qrcode_generator
import wifi_qrcode_generator.generator
from PIL import Image

ssid = "CLCoding_WIFI"
password = "supersecret123"
security = "WPA"

from wifi_qrcode_generator.generator import wifi_qrcode
qr = wifi_qrcode(ssid, False, security, password)

qr.make_image().save("wifi_qr.png")
Image.open("wifi_qr.png")
👉  @codeprogrammer

Interview question What do globals() and locals() do? Answer: Functions globals() and locals() return a dictionary with variables of the current scope. ▶️ globals() — provides access to all global names in the current module (i.e., variables, functions, and classes defined at the top level) ▶️ locals() — returns local variables in the current context: inside a function, class, or block They are useful for dynamic analysis, debugging, metaprogramming, and when working with eval() / exec(). tags: #interview ➡ @codeprogrammer

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This GitHub repo is a gold mine for EVERY data scientist! (full of hands-on and interactive tutorials) DS Interactive Python provides several dashboards to interactively learn about statistics, ML models, and other related theoretical concepts! Some key topics you can understand interactively: • PCA • Bagging and boosting • Linear regression and OLS • Bayesian and frequentist statistics • confidence intervals • clustering (kmeans, spectral clustering, etc.) • central limit theorem • neural networks (the backpropagation animation is really good) • and many more. Link: https://github.com/GeostatsGuy/DataScienceInteractivePython https://t.me/CodeProgrammer

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Data Science Formulas Cheat Sheet.pdf1.75 KB

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