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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 67 823 suscriptores, ocupando la posición 2 412 en la categoría Educación y el puesto 5 047 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 823 suscriptores.
Según los últimos datos del 08 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 50, y en las últimas 24 horas de -5, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.79%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.60% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 895 visualizaciones. En el primer día suele acumular 1 764 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 7.
- 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 09 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.
67 823
Suscriptores
-524 horas
+227 días
+5030 días
Archivo de publicaciones
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🔥 Trending Repository: awesome-deeplearning-resources
📝 Description: Deep Learning and deep reinforcement learning research papers and some codes
🔗 Repository URL: https://github.com/endymecy/awesome-deeplearning-resources
📖 Readme: https://github.com/endymecy/awesome-deeplearning-resources#readme
📊 Statistics:
🌟 Stars: 2.9K stars
👀 Watchers: 221
🍴 Forks: 666 forks
💻 Programming Languages: Not available
🏷️ Related Topics:
#nlp #video #reinforcement_learning #deep_learning #neural_network #code #paper #corpus #modelzoo================================== 🧠 By: https://t.me/DataScienceN
Repost from Github Top Repositories
🔥 Trending Repository: awesome-transformer-nlp
📝 Description: A curated list of NLP resources focused on Transformer networks, attention mechanism, GPT, BERT, ChatGPT, LLMs, and transfer learning.
🔗 Repository URL: https://github.com/cedrickchee/awesome-transformer-nlp
📖 Readme: https://github.com/cedrickchee/awesome-transformer-nlp#readme
📊 Statistics:
🌟 Stars: 1.1K stars
👀 Watchers: 41
🍴 Forks: 131 forks
💻 Programming Languages: Not available
🏷️ Related Topics:
#nlp #natural_language_processing #awesome #transformer #neural_networks #awesome_list #llama #transfer_learning #language_model #attention_mechanism #bert #gpt_2 #xlnet #pre_trained_language_models #gpt_3 #gpt_4 #chatgpt================================== 🧠 By: https://t.me/DataScienceM
Repost from Github Top Repositories
🔥 Trending Repository: SemanticSegmentation_DL
📝 Description: Resources of semantic segmantation based on Deep Learning model
🔗 Repository URL: https://github.com/tangzhenyu/SemanticSegmentation_DL
📖 Readme: https://github.com/tangzhenyu/SemanticSegmentation_DL#readme
📊 Statistics:
🌟 Stars: 1.1K stars
👀 Watchers: 77
🍴 Forks: 315 forks
💻 Programming Languages: Jupyter Notebook - Python - Shell - sed
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.me/DataScienceM
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python-docx: Create and Modify Word Documents #python
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
pip install python-docxfrom docx import Document
document = Document()
document.add_paragraph("It was a dark and stormy night.")
<docx.text.paragraph.Paragraph object at 0x10f19e760>
document.save("dark-and-stormy.docx")
document = Document("dark-and-stormy.docx")
document.paragraphs[0].text
'It was a dark and stormy night.'