Machine Learning
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
Mostrar más📈 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 41 659 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 41 659 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.
Carga de datos en curso...
| Fecha | Crecimiento de Suscriptores | Menciones | Canales | |
| 06 octubre | +13 | |||
| 05 octubre | +18 | |||
| 04 octubre | +40 | |||
| 03 octubre | +13 | |||
| 02 octubre | +17 | |||
| 01 octubre | +27 |
| 2 | Sin texto... | 636 |
| 3 | https://t.me/UdemySybot?start=ref_418788114
Get Free Courses 😁 | 544 |
| 4 | Sin texto... | 704 |
| 5 | Sin texto... | 2 269 |
| 6 | "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/ | 981 |
| 7 | 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 | 454 |
| 8 | "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 🤩 | 629 |
| 9 | https://t.me/UdemySybot?start=ref_418788114
🎓 Free Udemy courses every day — join me! | 875 |
| 10 | Sin texto... | 1 531 |
| 11 | Sin texto... | 1 335 |
| 12 | pandas_vs_polars_cheatsheet.png | 5 997 |
| 13 | 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 | 1 686 |
| 14 | 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 | 1 067 |
| 15 | Sin texto... | 1 808 |
| 16 | Sin texto... | 4 435 |
| 17 | Sin texto... | 4 960 |
| 18 | 🤖 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. | 2 693 |
| 19 | Sin texto... | 1 978 |
| 20 | Sin texto... | 2 446 |
