¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
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
Mostrar más📈 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 40 072 suscriptores, ocupando la posición 3 398 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 40 072 suscriptores.
Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 379, y en las últimas 24 horas de 30, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.92%. 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 770 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 24 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.
40 072
Suscriptores
+3024 horas
+337 días
+37930 días
Archivo de publicaciones
40 087
📌 How to Define the Modeling Scope of an Internal Credit Risk Model
🗂 Category: DATA SCIENCE
🕒 Date: 2026-02-25 | ⏱️ Read time: 9 min read
Dataset construction for Internal Ratings-Based (IRB) Probability of Default (PD) models
#DataScience #AI #Python
40 087
📌 Aliasing in Audio, Easily Explained: From Wagon Wheels to Waveforms
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-02-25 | ⏱️ Read time: 21 min read
Understanding the foundational distortion of digital audio from first principles, with worked examples and visual…
#DataScience #AI #Python
40 087
📌 Decisioning at the Edge: Policy Matching at Scale
🗂 Category: DATA SCIENCE
🕒 Date: 2026-02-24 | ⏱️ Read time: 13 min read
Policy-to-Agency Optimization with PuLP
#DataScience #AI #Python
40 087
Make AI write like a real human (and pass every checker) 100% free, No login, Unlimited use, Multi-language support Trusted by 50k+ users already. Your turn! 👉 https://unaimytext.com
40 087
📌 Optimizing Token Generation in PyTorch Decoder Models
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-02-24 | ⏱️ Read time: 17 min read
Hiding host-device synchronization via CUDA stream interleaving
#DataScience #AI #Python
40 087
📌 AI Bots Formed a Cartel. No One Told Them To.
🗂 Category: AGENTIC AI
🕒 Date: 2026-02-24 | ⏱️ Read time: 11 min read
Inside the research that shows algorithmic price-fixing isn’t a bug in the code. It’s a…
#DataScience #AI #Python
40 087
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40 087
📌 Optimizing Deep Learning Models with SAM
🗂 Category: DEEP LEARNING
🕒 Date: 2026-02-24 | ⏱️ Read time: 16 min read
A deep dive into the Sharpness-Aware-Minimization (SAM) algorithm and how it improves the generalizability of…
#DataScience #AI #Python
40 087
📌 Build Effective Internal Tooling with Claude Code
🗂 Category: LLM APPLICATIONS
🕒 Date: 2026-02-23 | ⏱️ Read time: 10 min read
Use Claude Code to quickly build completely personalized applications
#DataScience #AI #Python
40 087
📌 AI in Multiple GPUs: Gradient Accumulation & Data Parallelism
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-02-23 | ⏱️ Read time: 9 min read
Learn and implement gradient accum and data parallelism from scratch in PyTorch
#DataScience #AI #Python
40 087
Repost from Machine Learning with Python
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40 087
📌 PySpark for Pandas Users
🗂 Category: DATA ENGINEERING
🕒 Date: 2026-02-23 | ⏱️ Read time: 16 min read
Common Pandas operations and their equivalents in PySpark
#DataScience #AI #Python
40 087
📌 Is the AI and Data Job Market Dead?
🗂 Category: DATA SCIENCE
🕒 Date: 2026-02-23 | ⏱️ Read time: 8 min read
What you should be doing in the current job market
#DataScience #AI #Python
40 087
📌 The Reality of Vibe Coding: AI Agents and the Security Debt Crisis
🗂 Category: LLM APPLICATIONS
🕒 Date: 2026-02-22 | ⏱️ Read time: 6 min read
Why optimizing for speed over safety is leaving applications vulnerable, and how to fix it.
#DataScience #AI #Python
40 087
Repost from Machine Learning with Python
🏷 Comprehensive Data Science Roadmap Notes
✅ This roadmap is exactly the secret recipe you need to get out of confusion and know how to step-by-step prepare yourself for the job market.
🕡 From mastering Python and SQL to cleaning data and working with cloud tools, which are prerequisites for any project.
🕑 How to extract real analysis reports and strategies from raw data using statistics and visualization tools.
🕗 You will learn everything from machine learning and advanced algorithms to precise model evaluation.
🕙 Get familiar with neural networks, generative artificial intelligence, and language models to have a voice in today's modern world.
🕧 How to build real projects and portfolios that are exactly what hiring managers and big companies are looking for.
🌐 #DataScience #DataScience #pytorch #python #Roadmap
https://t.me/CodeProgrammer
40 087
Repost from Machine Learning with Python
🏷 Comprehensive Data Science Roadmap Notes
✅ This roadmap is exactly the secret recipe you need to get out of confusion and know how to step-by-step prepare yourself for the job market.
🕡 From mastering Python and SQL to cleaning data and working with cloud tools, which are prerequisites for any project.
🕑 How to extract real analysis reports and strategies from raw data using statistics and visualization tools.
🕗 You will learn everything from machine learning and advanced algorithms to precise model evaluation.
🕙 Get familiar with neural networks, generative artificial intelligence, and language models to have a voice in today's modern world.
🕧 How to build real projects and portfolios that are exactly what hiring managers and big companies are looking for.
⚡️ Download it Free 👇:
https://lve.to/gs6khab8jq
🌐 #DataScience #DataScience #pytorch #python #Roadmap
40 087
Repost from Machine Learning with Python
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40 087
📌 Architecting GPUaaS for Enterprise AI On-Prem
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-02-21 | ⏱️ Read time: 16 min read
Multi-tenancy, scheduling, and cost modeling on Kubernetes
#DataScience #AI #Python
40 087
📌 From Monolith to Contract-Driven Data Mesh
🗂 Category: DATA SCIENCE
🕒 Date: 2026-02-20 | ⏱️ Read time: 12 min read
A pragmatic journey using website analytics as a real-world example
#DataScience #AI #Python
40 087
📌 An End-to-End Guide to Beautifying Your Open-Source Repo with Agentic AI
🗂 Category: LLM APPLICATIONS
🕒 Date: 2026-02-20 | ⏱️ Read time: 17 min read
The guide to automated improvement of scientific and industrial repositories using open-source AI agents
#DataScience #AI #Python
