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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
Mostrar más📈 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 67 812 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 67 812 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.
67 812
Suscriptores
+924 horas
+587 días
+7730 días
Archivo de publicaciones
📈 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧
Why Logistic Regression is not regression
How Sigmoid (Logistic) function works
Binary vs Multiclass Logistic Regression
Decision boundaries and probability interpretation
Where Logistic Regression beats complex models
🎯 𝐓𝐨𝐩 𝟏𝟎 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐌𝐮𝐬𝐭-𝐊𝐧𝐨𝐰)
1️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘓𝘰𝘨𝘪𝘴𝘵𝘪𝘤 𝘙𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯?
2️⃣ 𝘞𝘩𝘺 𝘪𝘴 𝘓𝘰𝘨𝘪𝘴𝘵𝘪𝘤 𝘙𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯 𝘶𝘴𝘦𝘥 𝘧𝘰𝘳 𝘤𝘭𝘢𝘴𝘴𝘪𝘧𝘪𝘤𝘢𝘵𝘪𝘰𝘯, 𝘯𝘰𝘵 𝘳𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯?
3️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘚𝘪𝘨𝘮𝘰𝘪𝘥 𝘧𝘶𝘯𝘤𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘸𝘩𝘺 𝘪𝘴 𝘪𝘵 𝘯𝘦𝘦𝘥𝘦𝘥?
4️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘓𝘰𝘨 𝘓𝘰𝘴𝘴 / 𝘊𝘳𝘰𝘴𝘴-𝘌𝘯𝘵𝘳𝘰𝘱𝘺 𝘓𝘰𝘴𝘴?
5️⃣ 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘓𝘰𝘨𝘪𝘴𝘵𝘪𝘤 𝘙𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯 𝘢𝘯𝘥 𝘓𝘪𝘯𝘦𝘢𝘳 𝘙𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯?
6️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘢 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯 𝘣𝘰𝘶𝘯𝘥𝘢𝘳𝘺?
7️⃣ 𝘏𝘰𝘸 𝘥𝘰𝘦𝘴 𝘙𝘦𝘨𝘶𝘭𝘢𝘳𝘪𝘻𝘢𝘵𝘪𝘰𝘯 (𝘓1 𝘷𝘴 𝘓2) 𝘸𝘰𝘳𝘬 𝘪𝘯 𝘓𝘰𝘨𝘪𝘴𝘵𝘪𝘤 𝘙𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯?
8️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘖𝘥𝘥𝘴 𝘙𝘢𝘵𝘪𝘰 𝘢𝘯𝘥 𝘩𝘰𝘸 𝘥𝘰 𝘺𝘰𝘶 𝘪𝘯𝘵𝘦𝘳𝘱𝘳𝘦𝘵 𝘤𝘰𝘦𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘵𝘴?
9️⃣ 𝘏𝘰𝘸 𝘥𝘰 𝘺𝘰𝘶 𝘩𝘢𝘯𝘥𝘭𝘦 𝘤𝘭𝘢𝘴𝘴 𝘪𝘮𝘣𝘢𝘭𝘢𝘯𝘤𝘦?
🔟 𝘞𝘩𝘦𝘯 𝘴𝘩𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘢𝘷𝘰𝘪𝘥 𝘓𝘰𝘨𝘪𝘴𝘵𝘪𝘤 𝘙𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯?
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Repost from Machine Learning
The single most undervalued fact of linear algebra: matrices are graphs, and graphs are matrices.
Encoding matrices as graphs is a cheat code, making complex behavior simple to study.
https://t.me/DataScienceM
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
📐 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐕𝐞𝐜𝐭𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬 (𝐒𝐕𝐌)
🔹 What I covered today
What SVM is and how it works
Concept of hyperplane, margin, and support vectors
Hard margin vs Soft margin
Role of kernel trick
When SVM performs better than other classifiers
🎯 𝐓𝐨𝐩 𝟏𝟎 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐌𝐮𝐬𝐭-𝐊𝐧𝐨𝐰)
1️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘚𝘶𝘱𝘱𝘰𝘳𝘵 𝘝𝘦𝘤𝘵𝘰𝘳 𝘔𝘢𝘤𝘩𝘪𝘯𝘦 (𝘚𝘝𝘔)?
2️⃣ 𝘞𝘩𝘢𝘵 𝘢𝘳𝘦 𝘴𝘶𝘱𝘱𝘰𝘳𝘵 𝘷𝘦𝘤𝘵𝘰𝘳𝘴?
3️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘢 𝘮𝘢𝘳𝘨𝘪𝘯 𝘪𝘯 𝘚𝘝𝘔?
4️⃣ 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘩𝘢𝘳𝘥 𝘮𝘢𝘳𝘨𝘪𝘯 𝘢𝘯𝘥 𝘴𝘰𝘧𝘵 𝘮𝘢𝘳𝘨𝘪𝘯?
5️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘬𝘦𝘳𝘯𝘦𝘭 𝘵𝘳𝘪𝘤𝘬 𝘢𝘯𝘥 𝘸𝘩𝘺 𝘪𝘴 𝘪𝘵 𝘯𝘦𝘦𝘥𝘦𝘥?
6️⃣ 𝘊𝘰𝘮𝘮𝘰𝘯 𝘬𝘦𝘳𝘯𝘦𝘭𝘴 𝘶𝘴𝘦𝘥 𝘪𝘯 𝘚𝘝𝘔 (𝘓𝘪𝘯𝘦𝘢𝘳, 𝘗𝘰𝘭𝘺𝘯𝘰𝘮𝘪𝘢𝘭, 𝘙𝘉𝘍)?
7️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘳𝘰𝘭𝘦 𝘰𝘧 𝘊 (𝘳𝘦𝘨𝘶𝘭𝘢𝘳𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘱𝘢𝘳𝘢𝘮𝘦𝘵𝘦𝘳)?
8️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘨𝘢𝘮𝘮𝘢 𝘪𝘯 𝘙𝘉𝘍 𝘬𝘦𝘳𝘯𝘦𝘭?
9️⃣ 𝘊𝘢𝘯 #𝘚𝘝𝘔 𝘣𝘦 𝘶𝘴𝘦𝘥 𝘧𝘰𝘳 𝘳𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯? (𝘚𝘝𝘙)
🔟 𝘞𝘩𝘦𝘯 𝘴𝘩𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘢𝘷𝘰𝘪𝘥 𝘶𝘴𝘪𝘯𝘨 𝘚𝘝𝘔?
nature papers: 1400$
Q1 and Q2 papers 900$
Q3 and Q4 papers 500$
Doctoral thesis (complete) 700$
M.S thesis 300$
paper simulation 200$
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https://t.me/m/-nTmpj5vYzNk
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Q1 and Q2 papers 900$
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Doctoral thesis (complete) 700$
M.S thesis 300$
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https://t.me/m/-nTmpj5vYzNk
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🔰 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
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Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
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Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
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Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
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An active community group for discussing data challenges and networking with peers.
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The largest Arabic-speaking group for Python developers to share knowledge and help.
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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 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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https://t.me/Python53
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Professional Academic Writing & Simulation Services
https://t.me/DataScienceY
━━━━━━━━━━━━━━━━━━
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
https://t.me/m/-nTmpj5vYzNk
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
