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 67 829 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 67 829 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 06 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.

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Suscriptores

+924 horas

+587 días

+7730 días

1 767

Visitas de la publicación

~ 1 69524 horas

~ 2 25748 horas

2.60%

Tasa de compromiso

~ 3

Mensajes por día

Ads index

beta

Archivo de publicaciones

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Repost from Machine Learning with Python

The Python + Generative AI series by Azure AI Foundry has ended, but all materials are open Now you can calmly rewatch the recordings, download the slides, and try the code from each session — from LLM and RAG to AI agents and MCP. All resources are here: aka.ms/pythonai/resources 👉 @codeprogrammer

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🤖 Python libraries for AI agents — what to study If you want to develop AI agents in Python, it's important to understand the order of studying libraries. Start with LangChain, CrewAI or SmolAgents — they allow you to quickly assemble simple agents, connect tools, and test ideas. The next level is LangGraph, LlamaIndex and Semantic Kernel. These tools are already used for production systems: RAG, orchestration, and complex workflows. The most complex level is AutoGen, DSPy and A2A. They are needed for autonomous multi-agent systems and optimizing LLM pipelines. LangChain — simple agents, tools, and memory github.com/langchain-ai/langchain CrewAI — multi-agent systems with roles github.com/joaomdmoura/crewAI SmolAgents — lightweight agents for quick experiments github.com/huggingface/smolagents LangGraph — orchestration and stateful workflow github.com/langchain-ai/langgraph LlamaIndex — RAG and knowledge-agents github.com/run-llama/llama_index Semantic Kernel — AI workflow and plugins github.com/microsoft/semantic-kernel AutoGen — autonomous multi-agent systems github.com/microsoft/autogen DSPy — optimizing LLM pipelines github.com/stanfordnlp/dspy A2A — protocol for interaction between agents github.com/a2aproject/A2A https://t.me/CodeProgrammer 🌟

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PhD Students - Do you need datasets for your research? Here are 30 datasets for research from NexData. Use discount code for 20% off: G5W924C3ZI 1. Korean Exam Question Dataset for AI Training https://lnkd.in/d_paSwt7 2. Multilingual Grammar Correction Dataset https://lnkd.in/dV43iqTp 3. High quality video caption dataset https://lnkd.in/dY9kxkhx 4. 3D models and scenes datasets for AI and simulation https://lnkd.in/dT-zscH4 5. Image editing datasets – object removal, addition & modification https://lnkd.in/dd8iCGMS 6. QA dataset – visual & text reasoning https://lnkd.in/dc3TNWFD 7. English instruction tuning dataset https://lnkd.in/dTeTgd2M 8. Large scale vision language dataset for AI training https://lnkd.in/dBJuxazN 9. News dataset https://lnkd.in/dYBJe5gd 10. Global building photos dataset https://lnkd.in/dVJsDXnC 11. Facial landmarks dataset https://lnkd.in/dz_KGCS4 12. 3D Human Pose & Landmarks dataset https://lnkd.in/dXE9ir8Z 13. 3D Hand Pose & Gesture Recognition dataset https://lnkd.in/d_QdGGb9 14. 14. Driver monitoring dataset – dangerous, fatigue https://lnkd.in/d6kF-9PW 15. Japanese handwriting OCR dataset https://lnkd.in/dHnriqrH 16. American English Male voice TTS dataset https://lnkd.in/dqyvg862 17. Riddles and brain teasers dataset https://lnkd.in/dKBHY3DE 18. Chinese test questions text https://lnkd.in/dQpUd8xC 19. Chinese medical question answering data https://lnkd.in/dsbWUCpz 20. Multi-round interpersonal dialogues text data https://lnkd.in/dQiUq_Jg 21. Human activity recognition dataset https://lnkd.in/dHM52MfV 22. Facial expression recognition dataset https://lnkd.in/dqQAfMau 23. Urban surveillance dataset https://lnkd.in/dc2RCnTk 24. Human body segmentation dataset https://lnkd.in/d6sSrDxS 25. Fashion segmentation – clothing & accessories https://lnkd.in/dptNUTz8 26. Fight video dataset – action recognition https://lnkd.in/dnY_m5hZ 27. Gesture recognition dataset https://lnkd.in/dFVPivYg 28. Facial skin defects dataset https://lnkd.in/dKCbUvU6 29. Smoke detection and behaviour recognition dataset https://lnkd.in/ddGg56R4 30. Weight loss transformation video dataset https://lnkd.in/dqqT4ed9 https://t.me/CodeProgrammer 👾

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Был найден молодой и амбициозный канал про дизайн – @designkurilka. Уже завтра там начнётся любопытный челлендж. В течение месяца дизайнерка Эся будет проверять, может ли ИИ реально заменить дизайнера. Эксперимент будет на реальных задачах и тестовых из ВКонтакте, Яндекса и иностранных компаний. Поддерживаем, смотрим и подписываемся!

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Repost from Machine Learning with Python

Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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CNN vs Vision Transformer — The Battle for Computer Vision 👁⚡️ Two architectures. One goal: identify the cat. But they see things differently: 🧠 CNN (Convolutional Neural Network) · Scans the image with filters · Detects local patterns first (edges → textures → shapes) · Builds understanding layer by layer 🔄 Vision Transformer (ViT) · Splits image into patches (like words in a sentence) · Detects global patterns from the start · Sees the whole picture using attention mechanisms Same input. Same output. Different journey. CNNs think locally and build up. Transformers think globally from the get-go. Which one wins? Depends on the task — but both are shaping the future of how machines see. https://t.me/CodeProgrammer

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🚀 AI System Builders — finally something serious. A German company 🇩🇪 (Brainlancer GmbH) is launching a curated B2B AI platform on April 2026. This is NOT: ❌ a freelance marketplace ❌ an agency network This is: ✅ a verified AI builder network If you're accepted, you can offer your AI systems (e.g. Lead Gen, Customer Support, Recruiting Automation) for ~$2,499 setup + monthly maintenance. 👉 You focus on building systems 👉 Brainlancer handles clients & takes 20% --- 💡 If you can build real, end-to-end AI systems (not just prompts), this is for you. --- ⚡ Apply here (form takes 5–7 min): https://assesment.brainlancer.com/?src=tinvite 🎥 Quick overview video (thumbs up 👍): https://www.youtube.com/watch?v=jwhxqB-idsg&t=1s 👤 CEO (LinkedIn): https://www.linkedin.com/in/soner-catakli/ --- Early access is limited.

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How a CNN sees images simplified 🧠 1. Input → Image breaks into pixels (RGB numbers) 2. Feature Extraction · Convolution → Detects edges/patterns · ReLU → Kills negatives, adds non-linearity · Pooling → Shrinks data, keeps what matters 3. Fully Connected → Flattens features into meaning 4. Output → Probability scores: Cat? Dog? Car? Why powerful: Learns hierarchically — edges → shapes → objects Pixels to predictions. That's it. 👇 #DeepLearning #CNN #ComputerVision #AI

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🚀 𝐓𝐎𝐏 𝐑𝐀𝐆 𝐈𝐍𝐓𝐄𝐑𝐕𝐈𝐄𝐖 𝐐𝐔𝐄𝐒𝐓𝐈𝐎𝐍𝐒 𝐀𝐍𝐃 𝐀𝐍𝐒𝐖𝐄𝐑𝐒 ⁣⁣ 🔹 Advanced #RAG engineering concepts⁣⁣ • Multi-stage retrieval pipelines⁣⁣ • Agentic RAG vs classical RAG⁣⁣ • Latency optimization⁣⁣ • Security risks in enterprise RAG systems⁣⁣ • Monitoring and debugging production RAG systems⁣⁣ ⁣⁣ 📄 𝐓𝐡𝐞 𝐏𝐃𝐅 𝐜𝐨𝐧𝐭𝐚𝐢𝐧𝐬 𝟒𝟎 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐜𝐥𝐞𝐚𝐫 𝐞𝐱𝐩𝐥𝐚𝐧𝐚𝐭𝐢𝐨𝐧𝐬 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐛𝐨𝐭𝐡 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐚𝐧𝐝 𝐬𝐲𝐬𝐭𝐞𝐦 𝐝𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠.⁣⁣ ⁣⁣

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Repost from Python Courses & Resources

Python Tip: Operator Overloading This is a very important concept in Python.

Have you ever wondered how #Python understands what the + operator means? For numbers, it's addition; for strings, it's concatenation; for lists, it's union. This is operator overloading in action. Operator overloading means defining special behavior for operators (+, -, *, ==, etc.) in your user-defined classes. You determine how these operators should work with your objects.

👉 https://t.me/Python53

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