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 68 102 suscriptores, ocupando la posición 2 372 en la categoría Educación y el puesto 4 808 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 102 suscriptores.
Según los últimos datos del 27 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 112, y en las últimas 24 horas de 8, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.52%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.90% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 077 visualizaciones. En el primer día suele acumular 1 291 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
- 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 28 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.
68 102
Suscriptores
+824 horas
-727 días
+11230 días
Archivo de publicaciones
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💛 Top 10 Best Websites to Learn Machine Learning ⭐️
by [@codeprogrammer]
---
🧠 Google’s ML Course
🔗 https://developers.google.com/machine-learning/crash-course
📈 Kaggle Courses
🔗 https://kaggle.com/learn
🧑🎓 Coursera – Andrew Ng’s ML Course
🔗 https://coursera.org/learn/machine-learning
⚡️ Fast.ai
🔗 https://fast.ai
🔧 Scikit-Learn Documentation
🔗 https://scikit-learn.org
📹 TensorFlow Tutorials
🔗 https://tensorflow.org/tutorials
🔥 PyTorch Tutorials
🔗 https://docs.pytorch.org/tutorials/
🏛️ MIT OpenCourseWare – Machine Learning
🔗 https://ocw.mit.edu/courses/6-867-machine-learning-fall-2006/
✍️ Towards Data Science (Blog)
🔗 https://towardsdatascience.com
---
💡 Which one are you starting with? Drop a comment below! 👇
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Do you see yourself as a programmer, researcher, or engineer?
Repost from Machine Learning with Python
+1
These Google Colab-notebooks help to implement all machine learning algorithms from scratch 🤯
Repo: https://udlbook.github.io/udlbook/
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Build your own AI agent from scratch for free in 5 minutes
In this article, I will show you how to build your first AI agent from scratch using Google’s ADK (Agent Development Kit). This is an open-source framework that makes it easier to create agents, test them, add tools, and even build multi-agent systems.
Read: https://habr.com/en/articles/974212/
Repost from Data Analytics
This repository collects everything you need to use AI and LLM in your projects.
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Repo: https://github.com/KalyanKS-NLP/llm-engineer-toolkit
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😎 Machine Learning Cheatsheet — a structured ML guide!
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Repost from ADMINOTEKA
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