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 329...

📈 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 40 346 suscriptores, ocupando la posición 3 329 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 40 346 suscriptores.

Según los últimos datos del 11 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 393, y en las últimas 24 horas de 17, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.29%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.74% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 924 visualizaciones. En el primer día suele acumular 702 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 4.
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 12 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.

40 346

Suscriptores

+1724 horas

+1237 días

+39330 días

924

Visitas de la publicación

~ 70224 horas

~ 1 07048 horas

2.29%

Tasa de compromiso

~ 2

Mensajes por día

Ads index

beta

Archivo de publicaciones

40 352

📌 The End-to-End Data Scientist’s Prompt Playbook 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-08 | ⏱️ Read time: 10 min read Part 3: Prompts for docs, DevOps, and stakeholder communication

40 352

“It’s time to get these done.” Why are US lawmakers suddenly rushing crypto bills? What’s NEXT after BlackRock bought $416M in Bitcoin? No one tells you the full story – except here. The next move is days away. 👉 Find out first #إعلان InsideAds

40 352

📌 Become a Better Data Scientist with These Prompt Engineering Tips and Tricks 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-06-30 | ⏱️ Read time: 11 min read Part 1: prompt engineering for planning, cleaning, and EDA

40 352

📌 From Pixels to Plots 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-30 | ⏱️ Read time: 16 min read How I built an AI-powered prototype to turn images into insights

40 352

📌 Lessons Learned After 6.5 Years Of Machine Learning 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-06-30 | ⏱️ Read time: 7 min read Deep work, trends, data, and research

40 352

📌 A Gentle Introduction to Backtracking 🗂 Category: DATA SCIENCE 🕒 Date: 2025-06-30 | ⏱️ Read time: 7 min read Conceptual overview and hands-on examples

40 352

📌 Prescriptive Modeling Makes Causal Bets – Whether You Know it or Not! 🗂 Category: DATA SCIENCE 🕒 Date: 2025-06-30 | ⏱️ Read time: 16 min read An explanation of the causal assumption implicit in prescriptive modeling and how to satisfy it.

40 352

📌 From Reporting to Reasoning: How AI Is Rewriting the Rules of Data App Development 🗂 Category: DATA SCIENCE 🕒 Date: 2025-07-01 | ⏱️ Read time: 2 min read Explore the shift from static reports to intelligent apps with our first ebook.

40 352

📌 Revisiting Benchmarking of Tabular Reinforcement Learning Methods 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-07-01 | ⏱️ Read time: 9 min read Introducing a modular framework and improving model performance.

40 352

📌 Implementing IBCS rules in Power BI 🗂 Category: DATA VISUALIZATION 🕒 Date: 2025-07-01 | ⏱️ Read time: 12 min read Is there a way to use the out-of-the-box features of Power BI to be IBCS…

40 352

📌 An Introduction to Remote Model Context Protocol Servers 🗂 Category: DATA SCIENCE 🕒 Date: 2025-07-01 | ⏱️ Read time: 15 min read Writing, testing and using them.

40 352

📌 STOP Building Useless ML Projects – What Actually Works 🗂 Category: DATA SCIENCE 🕒 Date: 2025-07-01 | ⏱️ Read time: 7 min read How to find machine learning projects that will get you hired.

40 352

📌 How to Access NASA’s Climate Data — And How It’s Powering the Fight Against Climate Change Pt. 1 🗂 Category: DATA SCIENCE 🕒 Date: 2025-07-01 | ⏱️ Read time: 11 min read From architectural design to food security.

40 352

📌 How to Maximize Technical Events — NVIDIA GTC Paris 2025 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-07-02 | ⏱️ Read time: 10 min read Learn about my experience at NVIDIA GTC Paris 25, and how you can get the…

40 352

📌 Why We Should Focus on AI for Women 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-07-02 | ⏱️ Read time: 6 min read A simulation study on gender disparities entrenched in AI.

40 352

📌 Four AI Minds in Concert: A Deep Dive into Multimodal AI Fusion 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-07-02 | ⏱️ Read time: 36 min read Introduction: From System Architecture to Algorithmic Execution In my previous article, I explored the architectural…

40 352

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40 352

📌 Interactive Data Exploration for Computer Vision Projects with Rerun 🗂 Category: COMPUTER VISION 🕒 Date: 2025-07-02 | ⏱️ Read time: 6 min read Analyse dynamic signals in a computer vision pipeline in Python using OpenCV and Rerun

40 352

📌 Software Engineering in the LLM Era 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-07-02 | ⏱️ Read time: 13 min read On growing new software engineers, even when it’s inefficient

40 352

📌 Taking ResNet to the Next Level | ResNeXt 🗂 Category: DEEP LEARNING 🕒 Date: 2025-07-02 | ⏱️ Read time: 25 min read Understanding how ResNeXt improves upon ResNet, with a comprehensive PyTorch implementation guide