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 406...

📈 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 40 040 suscriptores, ocupando la posición 3 406 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 40 040 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.

40 040

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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📌 How to Call Rust from Python 🗂 Category: PROGRAMMING 🕒 Date: 2026-04-21 | ⏱️ Read time: 10 min read A guide to bridging the gap between ease of use and raw performance. #DataScience #AI #Python

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📌 Git UNDO : How to Rewrite Git History with Confidence 🗂 Category: PROGRAMMING 🕒 Date: 2026-04-21 | ⏱️ Read time: 24 min read For any data scientist who works in a team, being able to undo Git actions… #DataScience #AI #Python

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📌 DIY AI & ML: Solving The Multi-Armed Bandit Problem with Thompson Sampling 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-04-21 | ⏱️ Read time: 17 min read How you can build your own Thompson Sampling Algorithm object in Python and apply it… #DataScience #AI #Python

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📌 From Risk to Asset: Designing a Practical Data Strategy That Actually Works 🗂 Category: DATA SCIENCE 🕒 Date: 2026-04-20 | ⏱️ Read time: 11 min read How to turn data into a strategic asset that enables faster decisions, reduces uncertainty, and… #DataScience #AI #Python

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Repost from Machine Learning with Python

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📌 The LLM Gamble 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-04-20 | ⏱️ Read time: 8 min read Why it tickles your brain to use an LLM, and what that means for the… #DataScience #AI #Python

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📌 Context Payload Optimization for ICL-Based Tabular Foundation Models 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-04-20 | ⏱️ Read time: 16 min read Conceptual overview and practical guidance #DataScience #AI #Python

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📌 What Does the p-value Even Mean? 🗂 Category: DATA SCIENCE 🕒 Date: 2026-04-20 | ⏱️ Read time: 7 min read And what does it tell us? #DataScience #AI #Python

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📌 KV Cache Is Eating Your VRAM. Here’s How Google Fixed It With TurboQuant. 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-04-19 | ⏱️ Read time: 11 min read Explore the end-to-end pipeline of TurboQuant, a novel KV cache quantization framework. This overview breaks… #DataScience #AI #Python

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📌 Dreaming in Cubes 🗂 Category: DEEP LEARNING 🕒 Date: 2026-04-19 | ⏱️ Read time: 10 min read Generating Minecraft Worlds with Vector Quantized Variational Autoencoders (VQ-VAE) and Transformers #DataScience #AI #Python

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📌 Proxy-Pointer RAG: Structure Meets Scale at 100% Accuracy with Smarter Retrieval 🗂 Category: LARGE LANGUAGE MODEL 🕒 Date: 2026-04-19 | ⏱️ Read time: 14 min read Open source. 5-minute setup. Vector RAG done right—try it yourself. #DataScience #AI #Python

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📌 Your RAG System Retrieves the Right Data — But Still Produces Wrong Answers. Here’s Why (and How to Fix It). 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-04-18 | ⏱️ Read time: 17 min read Your RAG system is retrieving the right documents with perfect scores — yet it still… #DataScience #AI #Python

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📌 What It Actually Takes to Run Code on 200M€ Supercomputer 🗂 Category: DISTRIBUTED COMPUTING 🕒 Date: 2026-04-16 | ⏱️ Read time: 11 min read Inside MareNostrum V: SLURM schedulers, fat-tree topologies, and scaling pipelines across 8,000 nodes in a… #DataScience #AI #Python

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📌 How to Learn Python for Data Science Fast in 2026 (Without Wasting Time) 🗂 Category: PROGRAMMING 🕒 Date: 2026-04-18 | ⏱️ Read time: 8 min read What I wish I did at the beginning of my journey #DataScience #AI #Python

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📌 AI Agents Need Their Own Desk, and Git Worktrees Give Them One 🗂 Category: AGENTIC AI 🕒 Date: 2026-04-18 | ⏱️ Read time: 20 min read Git worktrees, parallel agentic coding sessions, and the setup tax you should be aware of #DataScience #AI #Python

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📌 A Practical Guide to Memory for Autonomous LLM Agents 🗂 Category: AGENTIC AI 🕒 Date: 2026-04-17 | ⏱️ Read time: 14 min read Architectures, pitfalls, and patterns that work #DataScience #AI #Python

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📌 6 Things I Learned Building LLMs From Scratch That No Tutorial Teaches You 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-04-17 | ⏱️ Read time: 11 min read From rank-stabilized scaling to quantization stability: A statistical and architectural deep dive into the optimizations… #DataScience #AI #Python

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📌 You Don’t Need Many Labels to Learn 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-04-17 | ⏱️ Read time: 10 min read What if an unsupervised model could become a strong classifier with only a handful of… #DataScience #AI #Python

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📌 Beyond Prompting: Using Agent Skills in Data Science 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-04-17 | ⏱️ Read time: 7 min read How I turned my eight-year weekly visualization habit into a reusable AI workflow #DataScience #AI #Python