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

40 365

📌 There and Back Again: An AI Career Journey 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-07-14 | ⏱️ Read time: 7 min read A full circle moment 30 years in the making

40 365

📌 Topic Model Labelling with LLMs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-07-14 | ⏱️ Read time: 6 min read Python tutorial for reproducible labeling of cutting-edge topic models with GPT4-o-mini.

40 365

📌 Accuracy Is Dead: Calibration, Discrimination, and Other Metrics You Actually Need 🗂 Category: DATA SCIENCE 🕒 Date: 2025-07-14 | ⏱️ Read time: 7 min read A deep dive into advanced evaluation for data scientists

40 365

📌 The Future of AI Agent Communication with ACP 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-07-15 | ⏱️ Read time: 17 min read A practical guide to connecting and coordinating multiple AI agents.

40 365

📌 Automating Deep Learning: A Gentle Introduction to AutoKeras and Keras Tuner 🗂 Category: DATA SCIENCE 🕒 Date: 2025-07-15 | ⏱️ Read time: 4 min read How to save time and boost your models with these two approachable AutoML libraries.

40 365

📌 From Equal Weights to Smart Weights: OTPO’s Approach to Better LLM Alignment 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-07-15 | ⏱️ Read time: 7 min read Using optimal transport to weight what matters most In LLM-generated responses

40 365

📌 Deploy a Streamlit App to AWS 🗂 Category: DATA SCIENCE 🕒 Date: 2025-07-15 | ⏱️ Read time: 16 min read Using the Elastic Beanstalk service

40 365

📌 How to Ensure Reliability in LLM Applications 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-07-15 | ⏱️ Read time: 7 min read Learn how to make your LLM applications more robust

40 365

📌 How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes 🗂 Category: DATA SCIENCE 🕒 Date: 2025-07-15 | ⏱️ Read time: 8 min read When numbers lie — and your metrics mislead you

40 365

📌 Do You Really Need a Foundation Model? 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-07-16 | ⏱️ Read time: 10 min read LLM or custom model: how should you choose the right solution?

40 365

📌 The Power of Building from Scratch 🗂 Category: AUTHOR SPOTLIGHTS 🕒 Date: 2025-07-16 | ⏱️ Read time: 5 min read Mauro Di Pietro discusses building AI agents with open-source tools, bridging theory and practice, and…

40 365

📌 3 Steps to Context Engineering a Crystal-Clear Project 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-07-16 | ⏱️ Read time: 7 min read Learn three easy steps for gaining an intelligent picture for any project by using the…

40 365

📌 How to Overlay a Heatmap on a Real Map with Python 🗂 Category: DATA VISUALIZATION 🕒 Date: 2025-07-16 | ⏱️ Read time: 9 min read Visualizing historical tornado trends

40 365

📌 Exploring Prompt Learning: Using English Feedback to Optimize LLM Systems 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-07-16 | ⏱️ Read time: 11 min read Prompt learning presents a compelling approach for continuous improvement of AI applications

40 365

📌 Midyear 2025 AI Reflection 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-07-16 | ⏱️ Read time: 7 min read Impressions on agentic AI progress and the AI-2027 Jobocalypse scenario

40 365

📌 Your 1M+ Context Window LLM Is Less Powerful Than You Think 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-07-17 | ⏱️ Read time: 9 min read Why working memory is a more important bottleneck than raw context window size

40 365

📌 Summer Must-Reads: The Data Science Edition 🗂 Category: THE VARIABLE 🕒 Date: 2025-07-17 | ⏱️ Read time: 4 min read Cool off with some engaging, enlightening reads.

40 365

📌 Don’t Waste Your Labeled Anomalies: 3 Practical Strategies to Boost Anomaly Detection Performance 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-07-17 | ⏱️ Read time: 15 min read A few labels go a long way in anomaly detection

40 365

📌 Estimating Disease Rates Without Diagnosis 🗂 Category: STATISTICS 🕒 Date: 2025-07-17 | ⏱️ Read time: 7 min read Immune genes as predictors of disease

40 365

📌 The Age of Self-Evolving AI Is Here 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-07-17 | ⏱️ Read time: 17 min read How Meta’s latest breakthrough lets models learn, adapt, and improve — all on their own