Machine Learning

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Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Red:Machine Learning with Python Siria215 Tecnologías y Aplicaciones3 145...

📈 Análisis del canal de Telegram Machine Learning

El canal Machine Learning (@machinelearning9) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 41 668 suscriptores, ocupando la posición 3 145 en la categoría Tecnologías y Aplicaciones y el puesto 215 en la región Siria.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 41 668 suscriptores.

Según los últimos datos del 05 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 533, y en las últimas 24 horas de 13, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 6.25%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.92% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 2 601 visualizaciones. En el primer día suele acumular 800 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 distance, insidead, gpu, learning, degree.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 06 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 Tecnologías y Aplicaciones.

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41 668

Suscriptores

+1324 horas

+1357 días

+53330 días

2 601

Visitas de la publicación

~ 80024 horas

~ 1 20948 horas

6.25%

Tasa de compromiso

~ 1

Mensajes por día

Ads index

beta

Archivo de publicaciones

41 668

Repost from Machine Learning with Python

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"Learning Mathematics: Why Memory, Practice, and Technique are More Important than Talent" To master mathematics, it is necessary to memorize a large amount of information, rules, methods of simplification, and problem-solving techniques. There is no other way. Theory is useful, but in moderation. It's like learning a language. If you focus too much on grammar, you will never learn to speak it fluently. https://algebrica.org/learning-mathematics/

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Repost from Machine Learning with Python

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

41 668

Repost from Machine Learning with Python

"How to Train a Neural Network" is a concise summary of the MIT course lectures on deep learning from 2024. It focuses on one of the fundamental questions in neural networks: how a model learns its weights. The summary examines the training process from a mathematical perspective. It covers topics such as forward propagation, loss functions, gradients, backpropagation, and gradient-based optimization methods. I believe this is an interesting resource for those who want to go beyond a general, intuitive understanding of neural networks and begin to delve into the mathematics that underlies their training. https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/mit6_7960_f24_lec2.pdf https://t.me/CodeProgrammer 🤩

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https://t.me/UdemySybot?start=ref_418788114 🎓 Free Udemy courses every day — join me!

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pandas_vs_polars_cheatsheet.png1.05 MB

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Repost from Machine Learning with Python

If you're just starting to learn machine learning and want to delve deeper into the mathematics required for machine learning and deep learning, I recommend trying this platform. It's something like LeetCode for machine learning. This is not an advertisement: I personally used it and decided to share it with you. https://deep-ml.com https://t.me/CodeProgrammer

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Repost from Machine Learning with Python

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🤖 A Practical Tip for ML Data Collection When building a machine learning project, getting enough useful data is often just as important as the model itself. If you're collecting public web data for a dataset, you may need to access the same source from different locations or test how location affects the data returned. A residential proxy can help with this by routing your requests through IPs from different regions. For example, with Python:

import requests

proxies = {
    "http": "http://USER:PASSWORD@HOST:PORT",
    "https": "http://USER:PASSWORD@HOST:PORT"
}

response = requests.get(
    "https://example.com",
    proxies=proxies
)

print(response.status_code)

Replace USER, PASSWORD, HOST, and PORT with your proxy credentials. 🚀 711Proxy provides real residential IPs across 200+ countries and regions, with SOCKS5 support and sticky sessions — useful for data collection, testing, and other location-based ML workflows. 🎁 1GB free for testing New users can use 711TRIAL to get 1GB of residential proxy traffic. 👉 https://www.711proxy.com After registration, contact 711Proxy support and mention “711TRIAL” to claim the trial. Available to eligible new users.

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