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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Red:📈 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 68 521 suscriptores, ocupando la posición 2 402 en la categoría Educación y el puesto 4 600 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 521 suscriptores.
Según los últimos datos del 29 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 74, 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 3.84%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.47% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 629 visualizaciones. En el primer día suele acumular 1 007 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 9.
- 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 30 octubre, 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.
68 521
Suscriptores
-524 horas
-847 días
+7430 días
Archivo de publicaciones
+4
🚀 𝐄𝐩𝐨𝐜𝐡 𝐯𝐬 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 – 𝐓𝐡𝐞 𝐌𝐨𝐬𝐭 𝐂𝐨𝐦𝐦𝐨𝐧𝐥𝐲 𝐂𝐨𝐧𝐟𝐮𝐬𝐞𝐝 𝐓𝐞𝐫𝐦𝐬!
When training a neural network, two words confuse most beginners:
🔹 𝐄𝐩𝐨𝐜𝐡
𝘈𝘯 𝘦𝘱𝘰𝘤𝘩 𝘮𝘦𝘢𝘯𝘴 𝘵𝘩𝘦 𝘮𝘰𝘥𝘦𝘭 𝘩𝘢𝘴 𝘴𝘦𝘦𝘯 𝘵𝘩𝘦 𝘦𝘯𝘵𝘪𝘳𝘦 𝘥𝘢𝘵𝘢𝘴𝘦𝘵 𝘰𝘯𝘤𝘦.
𝘐𝘧 𝘺𝘰𝘶 𝘵𝘳𝘢𝘪𝘯 𝘧𝘰𝘳 10 𝘦𝘱𝘰𝘤𝘩𝘴, 𝘺𝘰𝘶𝘳 𝘮𝘰𝘥𝘦𝘭 𝘨𝘰𝘦𝘴 𝘵𝘩𝘳𝘰𝘶𝘨𝘩 𝘵𝘩𝘦 𝘸𝘩𝘰𝘭𝘦 𝘥𝘢𝘵𝘢 10 𝘵𝘪𝘮𝘦𝘴.
🔹 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧
𝘈𝘯 𝘪𝘵𝘦𝘳𝘢𝘵𝘪𝘰𝘯 𝘪𝘴 𝘰𝘯𝘦 𝘸𝘦𝘪𝘨𝘩𝘵 𝘶𝘱𝘥𝘢𝘵𝘦, 𝘣𝘢𝘴𝘦𝘥 𝘰𝘯 𝘢 𝘴𝘪𝘯𝘨𝘭𝘦 𝘣𝘢𝘵𝘤𝘩 𝘰𝘧 𝘥𝘢𝘵𝘢.
If you have:
10,000 𝘳𝘦𝘤𝘰𝘳𝘥𝘴
𝘉𝘢𝘵𝘤𝘩 𝘴𝘪𝘻𝘦 = 100
👉 𝘛𝘩𝘦𝘯 𝘺𝘰𝘶 𝘨𝘦𝘵 100 𝘪𝘵𝘦𝘳𝘢𝘵𝘪𝘰𝘯𝘴 𝘱𝘦𝘳 𝘦𝘱𝘰𝘤𝘩.
✔️ 𝐒𝐢𝐦𝐩𝐥𝐞 𝐀𝐧𝐚𝐥𝐨𝐠𝐲
𝐓𝐡𝐢𝐧𝐤 𝐨𝐟 𝐠𝐨𝐢𝐧𝐠 𝐭𝐨 𝐭𝐡𝐞 𝐠𝐲𝐦:
𝐄𝐩𝐨𝐜𝐡 = 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐞𝐧𝐭𝐢𝐫𝐞 𝐰𝐨𝐫𝐤𝐨𝐮𝐭 𝐩𝐥𝐚𝐧 𝐨𝐧𝐜𝐞
𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 = 𝐝𝐨𝐢𝐧𝐠 𝐨𝐧𝐞 𝐬𝐞𝐭 𝐢𝐧𝐬𝐢𝐝𝐞 𝐭𝐡𝐚𝐭 𝐰𝐨𝐫𝐤𝐨𝐮𝐭
The model becomes stronger with every iteration, and improves overall with more epochs.
Read More: https://telegra.ph/Demystifying-Epochs--Iterations-in-Deep-Learning-11-24
👉 @codeprogrammer
Python For Data Science Cheat Sheet
#python #datascience #DataAnalysis
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Amazing NumPy Cheat Sheet Snippet with 100 exercises for practicing the concept to get hands on to clear the coding round in the interviews
#NumPy #codingexams
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I want to share a tool that I genuinely believe can make a real difference for anyone building apps: PacketSDK. Many developers have strong active-user bases but still struggle to increase revenue. That’s exactly why this solution stands out—it adds extra income without disrupting users or interfering with your existing monetization methods.
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Learn Complete #MatPlotLib with most asked #interview questions
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Create a Wi-Fi QR code in Python in a couple of seconds
pip install wifi_qrcode_generatorimport 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")❔ 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().
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This GitHub repo is a gold mine for EVERY data scientist!
(full of hands-on and interactive tutorials)
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• 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
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Repost from Python Courses & Resources
Create artificial intelligence using Python
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