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Machine Learning with Python

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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📈 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
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 👇👇   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

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DS Interview.pdf1.57 MB

These Google Colab-notebooks help to implement all machine learning algorithms from scratch 🤯 Repo: https://udlbook.github.i
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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
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. 120+ libraries, organized by development sta
This repository collects everything you need to use AI and LLM in your projects. 120+ libraries, organized by development stages: → Model training, fine-tuning, and evaluation → Deploying applications with LLM and RAG → Fast and scalable model launch → Data extraction, crawlers, and scrapers → Creating autonomous LLM agents → Prompt optimization and security Repo: https://github.com/KalyanKS-NLP/llm-engineer-toolkit 🥺 https://t.me/DataAnalyticsX

😎 Machine Learning Cheatsheet — a structured ML guide! There are no courses here, no unnecessary theory or long lectures, but there are clear formulas, algorithms, the logic of ML pipelines, and a neatly structured knowledge base. It's perfect for quickly refreshing your understanding of algorithms or having it handy as an ML cheat sheet during work. 📌 Here's the link: ml-cheatsheet.readthedocs.io 🚪 @codeprogrammer   | #resource

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Repost from ADMINOTEKA
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Господи, похоже, это канал о самых конченных и отвратительных выходках отечественных горе-бизнесменов. Если ты думал, что ниж
Господи, похоже, это канал о самых конченных и отвратительных выходках отечественных горе-бизнесменов. Если ты думал, что ниже Вайлдберрис, К&Б и Пятерочки падать уже некуда, то ты сильно заблуждался. Подписан — значит вооружен!

Best GitHub repositories to learn AI from scratch in 2026:
1. Andrej Karpathy https://github.com/karpathy/nn-zero-to-hero 2. Hugging Face Transformers https://github.com/huggingface/transformers 3. FastAI/fastbook https://github.com/fastai/fastbook 4. Made-With-ML https://github.com/GokuMohandas/Made-With-ML 5. ML System Design https://github.com/chiphuyen/machine-learning-systems-design 6. Awesome Generative AI guide https://github.com/aishwaryanr/awesome-generative-ai-guide 7. Dive into Deep Learning https://github.com/d2l-ai/d2l-en 👉 @codeprogrammer

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Collection of books on machine learning and artificial intelligence in PDF format Repo: https://github.com/Ramakm/AI-ML-Book-
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🤖 Machine Learning Tutorials Repository 1. Python 2. Computer Vision: Techniques, algorithms 3. NLP 4. Matplotlib 5. NumPy 6
🤖 Machine Learning Tutorials Repository 1. Python 2. Computer Vision: Techniques, algorithms 3. NLP 4. Matplotlib 5. NumPy 6. Pandas 7. MLOps 8. LLMs 9. PyTorch/TensorFlow git clone https://github.com/patchy631/machine-learning 🔗 GitHub: https://github.com/patchy631/machine-learning/tree/main ⭐️ https://t.me/DataScienceT

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