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 Siria225 Tecnologías y Aplicaciones3 332...

📈 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 40 323 suscriptores, ocupando la posición 3 332 en la categoría Tecnologías y Aplicaciones y el puesto 225 en la región Siria.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 323 suscriptores.

Según los últimos datos del 09 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 378, 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 2.23%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.95% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 897 visualizaciones. En el primer día suele acumular 788 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 10 julio, 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.

40 323

Suscriptores

+3024 horas

+1067 días

+37830 días

897

Visitas de la publicación

~ 78824 horas

~ 1 02448 horas

2.23%

Tasa de compromiso

~ 2

Mensajes por día

Ads index

beta

Archivo de publicaciones

40 332

📌 How to Benchmark DeepSeek-R1 Distilled Models on GPQA Using Ollama and OpenAI’s simple-evals 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-04-23 | ⏱️ Read time: 12 min read Set up and run the GPQA-Diamond benchmark on DeepSeek-R1’s distilled models locally to evaluate its…

40 332

📌 Exporting MLflow Experiments from Restricted HPC Systems 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-04-23 | ⏱️ Read time: 4 min read A workaround method that bypasses direct communication

40 332

📌 Predicting the NBA Champion with Machine Learning 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-24 | ⏱️ Read time: 10 min read Building a machine learning model to predict the NBA Champion and analyze the most impactful…

40 332

📌 Choose the Right One: Evaluating Topic Models for Business Intelligence 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-04-24 | ⏱️ Read time: 11 min read Python tutorial for evaluating top-notch bigram topic models in customer email classification

40 332

📌 How to Integrate AI into Complex Workflows 🗂 Category: THE VARIABLE 🕒 Date: 2025-04-24 | ⏱️ Read time: 3 min read This week, we focus on the nitty-gritty details of integrating AI workflows into new contexts.

40 332

📌 Government Funding Graph RAG 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-24 | ⏱️ Read time: 19 min read Graph visualisation for UK Research and Innovation (UKRI) funding, including NetworkX, PyVis and LlamaIndex graph…

40 332

📌 AWS: Deploying a FastAPI App on EC2 in Minutes 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-04-24 | ⏱️ Read time: 5 min read From zero to EC2: easy steps to launching an AWS Instance

40 332

📌 LLM Evaluations: from Prototype to Production 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-25 | ⏱️ Read time: 30 min read How to monitor the quality of your LLM product

40 332

📌 Behind the Magic: How Tensors Drive Transformers 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-04-25 | ⏱️ Read time: 4 min read The workflow Of tensors Inside Transformers

40 332

📌 A Step-By-Step Guide To Powering Your Application With LLMs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-04-25 | ⏱️ Read time: 8 min read Explore a hands-on guide to integrating large language models into real-world apps, not just read…

40 332

📌 Hands-on Multi Agent LLM Restaurant Simulation, with Python and OpenAI 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-04-28 | ⏱️ Read time: 12 min read This is how I used Large Language Models Agents to simulate an end-to-end restaurant process,…

40 332

📌 Adding Training Noise To Improve Detections In Transformers 🗂 Category: DEEP LEARNING 🕒 Date: 2025-04-28 | ⏱️ Read time: 8 min read Denoising, explained

40 332

📌 When OpenAI Isn’t Always the Answer: Enterprise Risks Behind Wrapper-Based AI Agents 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-28 | ⏱️ Read time: 8 min read Data privacy, compliance, and trust gaps in today’s AI agent integrations

40 332

📌 NumExpr: The “Faster than Numpy” Library Most Data Scientists Have Never Used 🗂 Category: PROGRAMMING 🕒 Date: 2025-04-28 | ⏱️ Read time: 8 min read A comparative performance test with NumPy

40 332

📌 Struggling to Land a Data Role in 2025? These 5 Tips Will Change That 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-28 | ⏱️ Read time: 7 min read Your dream data job isn’t ghosting you—you just need to search smart.

40 332

📌 How to Build an AI Budget-Planning Optimizer for Your 2026 CAPEX Review: LangGraph, FastAPI, and n8n 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-10 | ⏱️ Read time: 20 min read Email → n8n → LangGraph → FastAPI: turning budget requests into optimised CAPEX portfolios that…

40 332

📌 Why Task-Based Evaluations Matter 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-10 | ⏱️ Read time: 4 min read This article is adapted from a lecture series I gave at Deeplearn 2025: From Prototype…

40 332

📌 When A Difference Actually Makes A Difference 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-10 | ⏱️ Read time: 10 min read Bite-Sized Analytics for Business Decision-Makers (1)

40 332

📌 Fighting Back Against Attacks in Federated Learning 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-09-10 | ⏱️ Read time: 8 min read Lessons from a multi-node simulator

40 332

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