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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 67 821 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 67 821 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.
67 821
Suscriptores
+924 horas
+587 días
+7730 días
Archivo de publicaciones
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
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✅ https://t.me/Codeprogrammer
Repost from Data Analytics
📊 A comprehensive summary of the «Seaborn Library»
👨🏻💻 One of the best choices for any data scientist to convert data into clear and beautiful charts, so that they can better understand what the data is saying and also be able to present the results correctly and clearly to others, is the Seaborn library.
✅ A very user-friendly library for creating professional charts with minimal coding. It is built on top of Matplotlib but is simpler and easier to use than that.
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Repost from Data Analytics
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+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
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