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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📌 The Arithmetic of Productivity Boosts: Why Does a “40% Increase in Productivity” Never Actually Work? 🗂 Category: DATA SCIENCE 🕒 Date: 2026-04-07 | ⏱️ Read time: 5 min read Why does grand productivity promises never actually deliver? Is every product just bad, or is… #DataScience #AI #Python

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📌 Context Engineering for AI Agents: A Deep Dive 🗂 Category: AGENTIC AI 🕒 Date: 2026-04-07 | ⏱️ Read time: 8 min read How to optimize context, a precious finite resource for AI agents #DataScience #AI #Python

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📌 From 4 Weeks to 45 Minutes: Designing a Document Extraction System for 4,700+ PDFs 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-04-07 | ⏱️ Read time: 8 min read How a hybrid PyMuPDF + GPT-4 Vision pipeline replaced £8,000 in manual engineering effort, and… #DataScience #AI #Python

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📌 Behavior is the New Credential 🗂 Category: CYBERSECURITY 🕒 Date: 2026-04-06 | ⏱️ Read time: 7 min read We are living through a paradigm shift in how we prove we are who we… #DataScience #AI #Python

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📌 How to Run Claude Code Agents in Parallel 🗂 Category: LLM APPLICATIONS 🕒 Date: 2026-04-06 | ⏱️ Read time: 11 min read Learn how to apply coding agents in parallel to work more efficiently #DataScience #AI #Python

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📌 The Geometry Behind the Dot Product: Unit Vectors, Projections, and Intuition 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-04-06 | ⏱️ Read time: 12 min read The geometric foundations you need to understand the dot product #DataScience #AI #Python

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📌 Proxy-Pointer RAG: Achieving Vectorless Accuracy at Vector RAG Scale and Cost 🗂 Category: LARGE LANGUAGE MODEL 🕒 Date: 2026-04-05 | ⏱️ Read time: 23 min read A new way to build vector RAG—structure-aware and reasoning-capable #DataScience #AI #Python

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📌 A Data Scientist’s Take on the $599 MacBook Neo 🗂 Category: DATA SCIENCE 🕒 Date: 2026-04-05 | ⏱️ Read time: 7 min read Why it doesn’t fit my workflow but still makes sense for beginners #DataScience #AI #Python

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Repost from Machine Learning with Python

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📌 Building Robust Credit Scoring Models with Python 🗂 Category: DATA SCIENCE 🕒 Date: 2026-04-04 | ⏱️ Read time: 24 min read A Practical Guide to Measuring Relationships between Variables for Feature Selection in a Credit Scoring. #DataScience #AI #Python

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📌 Building a Python Workflow That Catches Bugs Before Production 🗂 Category: PROGRAMMING 🕒 Date: 2026-04-04 | ⏱️ Read time: 17 min read Using modern tooling to identify defects earlier in the software lifecycle. #DataScience #AI #Python

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Repost from Machine Learning with Python

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Repost from Machine Learning with Python

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📌 I Replaced Vector DBs with Google’s Memory Agent Pattern for my notes in Obsidian 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-04-03 | ⏱️ Read time: 13 min read Persistent AI memory without embeddings, Pinecone, or a PhD in similarity search. #DataScience #AI #Python

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📌 DenseNet Paper Walkthrough: All Connected 🗂 Category: DEEP LEARNING 🕒 Date: 2026-04-03 | ⏱️ Read time: 20 min read When we try to train a very deep neural network model, one issue that we… #DataScience #AI #Python

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Repost from Learn Python Coding

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