Machine Learning

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Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Red:Machine Learning with Python Siria232 Tecnologías y Aplicaciones3 402...

📈 Análisis del canal de Telegram Machine Learning

El canal Machine Learning (@machinelearning9) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 40 057 suscriptores, ocupando la posición 3 402 en la categoría Tecnologías y Aplicaciones y el puesto 232 en la región Siria.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 057 suscriptores.

Según los últimos datos del 22 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 372, y en las últimas 24 horas de 2, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.94%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.16% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 775 visualizaciones. En el primer día suele acumular 466 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
Intereses temáticos: El contenido se centra en temas clave como distance, insidead, gpu, learning, degree.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 23 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 Tecnologías y Aplicaciones.

40 057

Suscriptores

+224 horas

+237 días

+37230 días

775

Visitas de la publicación

~ 46624 horas

~ 62048 horas

1.94%

Tasa de compromiso

~ 3

Mensajes por día

Ads index

beta

Archivo de publicaciones

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📌 Building Robust Credit Scoring Models (Part 3) 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-20 | ⏱️ Read time: 18 min read Handling outliers and missing values in borrower data using Python. #DataScience #AI #Python

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📌 The Basics of Vibe Engineering 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-19 | ⏱️ Read time: 14 min read Building products without the coding part #DataScience #AI #Python

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📌 Vibe Coding with AI: Best Practices for Human-AI Collaboration in Software Development 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-19 | ⏱️ Read time: 16 min read Accelerate coding with AI while staying in control and building reliable, production-ready software. #DataScience #AI #Python

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📌 Linear Regression Is Actually a Projection Problem, Part 1: The Geometric Intuition 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-19 | ⏱️ Read time: 14 min read A visual guide to vectors and projections #DataScience #AI #Python

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📌 Beyond Prompt Caching: 5 More Things You Should Cache in RAG Pipelines 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-19 | ⏱️ Read time: 13 min read A practical guide to caching layers across the RAG pipeline, from query embeddings to full… #DataScience #AI #Python

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Repost from Machine Learning with Python

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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📌 Why You Should Stop Worrying About AI Taking Data Science Jobs 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-18 | ⏱️ Read time: 8 min read It’s all just fearmongering #DataScience #AI #Python

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📌 The New Experience of Coding with AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-18 | ⏱️ Read time: 12 min read The seduction of AI code assistants #DataScience #AI #Python

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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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📌 Two-Stage Hurdle Models: Predicting Zero-Inflated Outcomes 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-18 | ⏱️ Read time: 20 min read Why one model can’t do two jobs #DataScience #AI #Python

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Listen, here’s the crazy part: while everyone’s scared BTC might crash, insiders just dropped $350 MILLION buying the dip. Unreal, right? The market’s playing a game where the biggest shorts might implode first-like a loaded gun cocked and ready. This isn’t hype, it’s cold, hard data from 14 years of trading wisdom. Wanna see how the pros move and actually win? Check this out 👉 Scalping Kings No fluff, just profits. #ad InsideAds

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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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📌 Self-Hosting Your First LLM 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-17 | ⏱️ Read time: 20 min read Privacy. Cost. Customization. Everything you need to know—step by step. #DataScience #AI #Python

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📌 How to Effectively Review Claude Code Output 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-17 | ⏱️ Read time: 7 min read Get more out of your coding agents by making reviewing more efficient #DataScience #AI #Python

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Time Complexity of 10 Most Popular ML Algorithms Know What You're Waiting For ⏳🧠

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Repost from Machine Learning with Python

🚀 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.⁣⁣ ⁣⁣ https://t.me/CodeProgrammer

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📌 How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment 🗂 Category: DEEP LEARNING 🕒 Date: 2026-03-17 | ⏱️ Read time: 18 min read Most neuro-symbolic systems inject rules written by humans. But what if a neural network could… #DataScience #AI #Python

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📌 Introducing Gemini Embeddings 2 Preview 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-17 | ⏱️ Read time: 10 min read One embedding model to rule them all #DataScience #AI #Python

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📌 How to Build a Production-Ready Claude Code Skill 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-16 | ⏱️ Read time: 11 min read What I learned building and distributing my first Skill from scratch #DataScience #AI #Python

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📌 Follow the AI Footpaths 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-16 | ⏱️ Read time: 6 min read Shadow AI and the desire paths of modern work #DataScience #AI #Python