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 68 536 suscriptores, ocupando la posición 2 402 en la categoría Educación y el puesto 4 600 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 536 suscriptores.
Según los últimos datos del 29 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 74, 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 3.84%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.47% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 629 visualizaciones. En el primer día suele acumular 1 007 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 9.
- 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 30 octubre, 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.
68 536
Suscriptores
-524 horas
-847 días
+7430 días
Archivo de publicaciones
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Кто я и зачем Я здесь
Дмитрий Мачихин, основатель компании BitOK (Бит окей). Предприниматель и инвестор в сфере финансов, web3 и игр. Пишу для себя и для вас. Иногда наступаю на горло мошенникам и врагам народа.
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Repost from Udemy Free
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𝗧𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝘄𝗮𝗻𝘁 𝗿𝗲𝗳𝗲𝗿𝗿𝗮𝗹𝘀 𝗮𝗻𝗱 𝗝𝗼𝗯𝘀 𝗮𝗻𝗱 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽𝘀 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 𝗳𝗿𝗼𝗺 𝗧𝗼𝗽 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗕𝗮𝘀𝗲𝗱, 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗕𝗮𝘀𝗲𝗱 𝗮𝗻𝗱 𝗦𝘁𝗮𝗿𝘁 𝘂𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗹𝗶𝗸𝗲 𝗔𝗺𝗮𝘇𝗼𝗻, 𝗚𝗼𝗼𝗴𝗹𝗲, 𝗔𝗽𝗽𝗹𝗲, 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁, 𝗜𝗕𝗠, 𝗧𝗖𝗦, 𝗖𝗼𝗴𝗻𝗶𝘇𝗮𝗻𝘁, 𝗪𝗶𝗽𝗿𝗼, 𝗖𝗧𝗦, 𝗚𝗼𝗹𝗱𝗺𝗮𝗻 𝗦𝗮𝗰𝗵𝘀, 𝗢𝗹𝗮, 𝗨𝗯𝗲𝗿, 𝗭𝗼𝗺𝗮𝘁𝗼, 𝗦𝘄𝗶𝗴𝗴𝘆, 𝘂𝗽𝗚𝗿𝗮𝗱, 𝗖𝘂𝗿𝗲 𝗙𝗶𝘁, 𝗛𝗮𝗰𝗸𝗲𝗿𝗿𝗮𝗻𝗸, 𝗚𝗲𝗲𝗸𝘀𝗳𝗼𝗿𝗴𝗲𝗲𝗸𝘀 𝗮𝗻𝗱 𝗺𝗮𝗻𝘆 𝗺𝗼𝗿𝗲, 𝗰𝗮𝗻 𝗷𝗼𝗶𝗻 𝘁𝗵𝗲𝘀𝗲 𝗯𝗲𝗹𝗼𝘄 𝗰𝗵𝗮𝗻𝗻𝗲𝗹𝘀
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https://t.me/jobsandinternshipsupdates
💛 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! 👇
#MachineLearning #LearnML #DataScience #AI #TechWithRam
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/
👉 @codeprogrammer
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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