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 68 103 suscriptores, ocupando la posición 2 374 en la categoría Educación y el puesto 4 765 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 103 suscriptores.
Según los últimos datos del 28 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 75, y en las últimas 24 horas de -18, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.69%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.70% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 194 visualizaciones. En el primer día suele acumular 1 155 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 29 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.
68 103
Suscriptores
-1824 horas
-907 días
+7530 días
Archivo de publicaciones
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🚀 Master Data Science & Programming!
Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
🔰 Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer
🔖 Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.me/DataScienceM
🧠 Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
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🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
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💾 Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
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😀 ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
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💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
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🐍 Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
🖊 Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
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📺 Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
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Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
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Admin: @HusseinSheikho
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Repost from Machine Learning
🔖 40 NumPy methods that cover 95% of tasks
A convenient cheat sheet for those who work with data analysis and ML.
Here are collected the main functions for:
▶️ Creating and modifying arrays; ▶️ Mathematical operations; ▶️ Working with matrices and vectors; ▶️ Sorting and searching for values.Save it for yourself — it will come in handy when working with NumPy. tags: #NumPy #Python ➡ @DataScienceM
nature papers: 1400$
Q1 and Q2 papers 900$
Q3 and Q4 papers 500$
Doctoral thesis (complete) 700$
M.S thesis 300$
paper simulation 200$
Contact me
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nature papers: 1400$
Q1 and Q2 papers 900$
Q3 and Q4 papers 500$
Doctoral thesis (complete) 700$
M.S thesis 300$
paper simulation 200$
Contact me
https://t.me/m/-nTmpj5vYzNk
200$ to 20k$ SOL Challenge!
As promised, i will do another challenge for those who missed the previous one!
Last one we completed in 6 days, let’s do this one even quicker!
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This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
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Repost from Python Courses & Resources
Learn how LLMs work in less than 10 minutes
And honestly? This is probably the best visualization of #LLMs ever made.
https://t.me/Python53
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Awesome open-source project to learn more about Generative Adversarial Networks.
We found this interactive website that shows you visually how #GANs work.
GAN Lab Website: https://lnkd.in/eYV8QvrJ
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All assignments for the #Stanford The Modern Software Developer course are now available online.
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How to test code without a real database
During unit testing, connecting to a real DB is unnecessary: • tests run slowly • become unstable • require a working serverIt is much better to mock the call to
pandas.read_sql and return dummy data
Example function:
def query_user_data(user_id):
query = f"SELECT id, name FROM users WHERE id = {user_id}"
return pd.read_sql(query, "postgresql://localhost/mydb")from unittest.mock import patch
import pandas as pd
@patch("pandas.read_sql")
def test_database_query_mocked(mock_read_sql):
mock_read_sql.return_value = pd.DataFrame(
{"id": [123], "name": ["Alice"]}
)
result = query_user_data(user_id=123)
assert result["name"].iloc[0] == "Alice"